中文摘要：隨著全面健康理念的不斷普及和深化，人們越來越意識到自己是個(gè)人“健康第一責(zé)任人”，主動對網(wǎng)絡(luò)健康信息的搜索、獲取與選擇利用逐漸成為人們健康生活的重要組成部分。然而，我國網(wǎng)絡(luò)用戶的健康信息素養(yǎng)整體偏低，健康信息辨別能力也偏弱，不容易從質(zhì)量參差不齊的網(wǎng)絡(luò)信息海洋中搜索到高質(zhì)量健康信息，這一問題嚴(yán)重影響了我國民眾的健康生活，也成為黨和國家關(guān)注的重要民生問題之一。也正因如此，如何幫助民眾搜索并獲取高質(zhì)量健康信息、增長健康知識、提升解決健康問題的能力、保障健康生活水平已成為情報(bào)學(xué)領(lǐng)域關(guān)注的熱點(diǎn)問題。而已有相關(guān)研究對不同用戶在不同任務(wù)情境下網(wǎng)絡(luò)健康信息交互行為、交互感知以及交互質(zhì)量間的差異或關(guān)聯(lián)關(guān)系分析、用戶獲取不同質(zhì)量網(wǎng)絡(luò)健康信息的影響因素、用戶網(wǎng)絡(luò)健康信息交互質(zhì)量預(yù)測模型及有效干預(yù)策略與機(jī)制等方面的研究成果還很缺乏?；谶@樣的社會背景與學(xué)術(shù)背景，本研究希望通過分析并回答以下4個(gè)研究問題：①不同個(gè)體特征用戶網(wǎng)絡(luò)健康信息交互行為有哪些特征？②不同任務(wù)情境下用戶網(wǎng)絡(luò)健康信息交互行為有哪些特征？③不同任務(wù)情境下用戶網(wǎng)絡(luò)健康信息搜索時(shí)的交互行為、交互感知與交互質(zhì)量間的關(guān)系是怎樣的？④用戶網(wǎng)絡(luò)健康信息交互質(zhì)量如何被有效預(yù)測，低質(zhì)量交互行為又如何被引導(dǎo)？旨在促進(jìn)健康信息系統(tǒng)個(gè)性化、智能化的設(shè)計(jì)與優(yōu)化，從而幫助民眾高效獲取高質(zhì)量健康信息、學(xué)習(xí)和積累健康知識，提升國民健康信息素養(yǎng)與生活健康水平，助力“健康中國2030”戰(zhàn)略實(shí)現(xiàn)。 基于以上研究背景與問題，研究首先對相關(guān)概念、相關(guān)基礎(chǔ)理論及已有相關(guān)研究進(jìn)行了梳理與回顧，進(jìn)一步分析了研究開展的必要性與可行性。然后，在借鑒已有研究基礎(chǔ)上，采用用戶實(shí)驗(yàn)方法，通過問卷、半結(jié)構(gòu)化訪談、用戶搜索行為記錄軟件等方式或工具對用戶個(gè)體特征及用戶網(wǎng)絡(luò)健康信息搜索過程中的交互感知、交互行為、交互質(zhì)量等多種不同類型數(shù)據(jù)進(jìn)行了收集，并針對數(shù)據(jù)結(jié)構(gòu)特征與研究目標(biāo)，采取了相應(yīng)的數(shù)據(jù)分析方法，對相關(guān)問題展開了分析，得到了以下結(jié)果與結(jié)論： ①不同個(gè)體特征用戶（性別、學(xué)歷、學(xué)科、信息檢索學(xué)習(xí)經(jīng)歷、計(jì)算機(jī)使用頻率、信息搜索經(jīng)驗(yàn)、健康信息素養(yǎng)與認(rèn)知風(fēng)格）在選擇不同健康信息類型（不同信息形式）與健康信息源時(shí)均存在一定偏好（研究問題1）；任務(wù)屬性/類型對用戶健康信息類型、健康信息源類型選擇也存在顯著影響（研究問題2）。②任務(wù)復(fù)雜度、任務(wù)產(chǎn)品類型與健康信息話題類型等任務(wù)屬性不僅對用戶網(wǎng)絡(luò)健康信息搜索時(shí)的內(nèi)容交互行為、系統(tǒng)交互行為有顯著的主效應(yīng)，而且不同屬性間的交互效應(yīng)也一定程度上影響了用戶交互行為（研究問題2）。③感知任務(wù)困難程度、方法和過程熟悉程度、感知任務(wù)復(fù)雜度、搜索經(jīng)歷豐富程度、信心程度、信息有用性判斷難度、獲取信息的認(rèn)知強(qiáng)度、確定有用信息努力程度、任務(wù)感知成功、任務(wù)感知挫敗感、任務(wù)感知滿意等交互感知子維度均與交互行為存在顯著相關(guān)關(guān)系，而且鼠標(biāo)點(diǎn)擊最大間隔時(shí)間、任務(wù)持續(xù)時(shí)間、查詢修改次數(shù)、點(diǎn)擊鏈接次數(shù)、使用推薦查詢數(shù)、鼠標(biāo)移動像素量、保存信息條目/瀏覽信息條目、訪問檢索系統(tǒng)個(gè)數(shù)、鼠標(biāo)滑輪滾動次數(shù)與鼠標(biāo)左鍵點(diǎn)擊次數(shù)等交互行為指標(biāo)可作為自變量來解釋和預(yù)測用戶交互感知狀態(tài)（研究問題3）。④不同任務(wù)屬性對用戶網(wǎng)絡(luò)健康信息搜索過程中的任務(wù)交互質(zhì)量有明顯的主效應(yīng)作用，不同任務(wù)屬性間的交互效應(yīng)對交互質(zhì)量也有顯著影響（研究問題3）。⑤用戶搜索網(wǎng)絡(luò)健康信息過程中的交互感知對交互質(zhì)量有顯著的影響，且多重線性回歸分析結(jié)果表明信息有用性判斷難度、獲取信息的認(rèn)知強(qiáng)度、方法和過程熟悉程度與感知任務(wù)復(fù)雜度能有效解釋交互質(zhì)量的高低變化（研究問題3）。⑥交互行為各項(xiàng)指標(biāo)與交互質(zhì)量存在不同程度的相關(guān)關(guān)系，且多重線性回歸模型顯示鼠標(biāo)移動像素量、鼠標(biāo)左鍵點(diǎn)擊次數(shù)、瀏覽條目總數(shù)、鼠標(biāo)滑輪滾動次數(shù)、使用推薦查詢數(shù)、鍵盤輸入最大間隔時(shí)間等交互行為指標(biāo)可有效解釋交互質(zhì)量的變化（研究問題3）。⑦交互感知、交互行為對交互質(zhì)量影響作用路徑分析結(jié)果表明，交互感知?交互質(zhì)量、交互行為?交互質(zhì)量的直接效應(yīng)顯著，而交互感知?交互行為?交互質(zhì)量的間接效應(yīng)不具有顯著的統(tǒng)計(jì)學(xué)意義（研究問題3）。⑧以相關(guān)性變量（包括線性回歸關(guān)系自變量）與任務(wù)屬性為輸入變量的1層隱含層BP神經(jīng)網(wǎng)絡(luò)模型與以相關(guān)性變量（包括線性回歸關(guān)系自變量）、任務(wù)屬性與個(gè)體特征為輸入變量的2層隱含層BP神經(jīng)網(wǎng)絡(luò)模型可以有效預(yù)測不同任務(wù)完整會話的交互質(zhì)量，而基于2分鐘時(shí)間切片的2層隱藏層BP神經(jīng)網(wǎng)絡(luò)模型在交互質(zhì)量實(shí)時(shí)預(yù)測上有較好的表現(xiàn)（研究問題4）。 基于以上發(fā)現(xiàn)，研究進(jìn)一步對用戶網(wǎng)絡(luò)健康信息交互行為引導(dǎo)機(jī)制進(jìn)行了分析和探索，構(gòu)建了基于完整任務(wù)會話預(yù)測模型與基于任務(wù)會話時(shí)間切片實(shí)時(shí)預(yù)測模型的交互行為引導(dǎo)機(jī)制，并對低質(zhì)量交互與交互行為引導(dǎo)策略觸發(fā)機(jī)制進(jìn)行了分析（研究問題4）。考慮實(shí)驗(yàn)研究發(fā)現(xiàn)用戶在進(jìn)行不同健康信息搜索任務(wù)時(shí)交互質(zhì)量整體偏低，且用戶健康信息素養(yǎng)整體上不高。研究基于分析結(jié)果與結(jié)論，并結(jié)合社會認(rèn)知理論，針對用戶健康信息素養(yǎng)與交互質(zhì)量提升策略也進(jìn)行了探討（研究問題4）。 本研究的相關(guān)結(jié)果與結(jié)論及其帶來的啟示，一方面，可以促進(jìn)用戶網(wǎng)絡(luò)健康信息行為研究的深化發(fā)展與相關(guān)知識體系的完善，同時(shí)也為未來相關(guān)研究的推進(jìn)提供了參考；另一方面，相關(guān)策略與機(jī)制的提出為健康信息系統(tǒng)個(gè)性化搜索功能設(shè)計(jì)與優(yōu)化、用戶健康信息素養(yǎng)與交互質(zhì)量的提高提供了方法路徑與實(shí)施策略，從而幫助用戶高效獲取高質(zhì)量健康信息，學(xué)習(xí)健康知識，科學(xué)合理地解決健康問題，進(jìn)而提高公民整體健康素養(yǎng)和健康生活水平。最后，文章根據(jù)研究的局限與不足對未來研究進(jìn)行了展望。圖105幅，表156個(gè)，參考文獻(xiàn)404篇。  英文摘要：With the popularization and deepening of the concept of comprehensive health, people are increasingly aware that they are the first person responsible for their health, and the active searching, acquiring and selecting of health information on the Web are becoming an important part of people's healthy life. However, the health information literacy of Web users in China is low, and the discrimination ability of health information quality is also weak, which makes it difficult to search high-quality health information from the Web. All of this seriously affects the healthy living condition of Chinese people, resulting in a serious livelihood issues concerned by the national government. Under this background, how to help the public or web users to search and obtain high-quality health information, increase their health knowledge, improve the ability to solve health problems, and keep healthy has become a hot research issue in the field of Library and Information Science (LIS). Unfortunately, few existing researches have been conducted on the correlations or differences between web health information interactive behavior，interaction perception and interaction quality of different users in different task situation, and the factors which influencing users’ access to different quality of web health information. The prediction model of user interaction quality when they searching for web health information, and effective intervention strategies and mechanisms are also rarely involved. Considering these social and academic concerns, this study aims to find answers or solutions for the following four research questions: (1) What are the differences between Web health information interactive behaviors of users with different individual characteristics? (2) What are the differences between Web health information interactive behaviors while users are searching for different task? (3) What are the relationships between users' interactive behavior, interaction perception and interaction quality when searching for Web health information under different task situations? (4) How can the interaction quality of users searching for Web health information be effectively predicted, and how can low-quality interactive behaviors be led to a right way? By answering these questions, we hope to help people learn and increase health knowledge, improve the national health information literacy and the health level of people’s daily life, contributing to the realization of "Healthy China 2030" strategy. Based on the above research background and questions, this paper firstly reviews and summarizes relevant concepts, relevant basic theories and existing researches, and further analyzes the necessity and feasibility of current study. Then, it introduces how the user experiments was designed and conducted for answering the research questions. In the experiment, questionnaires, semi-structured interviews, user search behavior recording software are used to collect the data of user's individual characteristics, interaction perception, interactive behavior and interaction quality in the process of user searching Web health information. Based on the characteristics of data structure and research objectives, corresponding data analysis methods are adopted to analyze related problems and the following findings are obtained: (1) Users with different individual characteristics (gender, educational background, discipline, learning experience of information retrieval, frequency of computer using, information searching experience, health information literacy and cognitive style) have certain preferences when selecting different types of health information and health information sources(for RQ1), and task attribute/type also has significant influence on users’ selection of health information form and health information source(for RQ2). (2) Task attributes, such as task complexity, task product type and topic type of health information, not only have significant main effects on users' interactive behavior, including content interactive behavior and system interactive behavior, but also have the interaction effects between different attributes on users' interactive behaviors to some extent (for RQ2). (3) Except for familiarity with the subject, all the sub-dimensions of interaction perception are significantly correlated with interactive behavior. In addition, interactive behavior indicators, such as Maximum time between mouse clicks、Time on task、Number of query modification、Number of hyperlink clicks、Number of recommended query accepted、Mouse movement、Saving useful document or page/ Total number of items viewed、Number of IR system consulted、Wheel scrolling and Mouse clicks-left button, can be used as independent variables to explain and predict user interaction perception state (for RQ3). (4) Different task attributes have a significant main effect on task interaction quality, and the interaction effect between different task attributes also has a significant effect on task interaction quality (for RQ3). (5) The interaction perception that users have in the process of searching for Web health information has a significant impact on the interaction quality, and the results of multiple linear regression analysis show that the Difficulty of information usefulness judgment, the Cognitive load of acquiring information, the Familiarity of methods and processes, and Perceived task complexity can effectively explain the change of the interaction quality (for RQ3). (6) Various indicators of interactive behavior are correlated with interaction quality to different degrees, and the multiple linear regression model shows that Mouse movement、Mouse clicks-left button、Total number of items viewed、Wheel scrolling、Number of recommended query accepted、Maximum time between keystrokes can effectively explain the change of interaction quality (for RQ3). (7) The path analysis of interaction perception and interactive behavior on interaction quality shows that the direct effects of "interaction perception?interaction quality" and "interactive behavior?interaction quality" are significant, while the indirect effects of "interaction perception? interactive behavior? interaction quality" are not statistically significant (for RQ3). (8) One hidden layer of BP neural network model, with correlation variables and the task properties as input variables, and 2 hidden layer of BP neural network model, with the correlation variable, the task properties and individual characteristics as the input variables, can effectively predict the interaction quality of a complete task session. And the two-layer hidden layer BP neural network model based on 2-minute time slice has better performance in real-time prediction of interaction quality (for RQ4). Based on the above findings, users’ Web health information interaction behavior leading mechanism is analyzed and explored， and two interactive behavior leading mechanisms, basing on complete task session prediction model and real-time task session time slice prediction model, are constructed (for RQ4). Considering that current experimental study finding that the interaction quality of users in different health information search tasks are generally low, and the health information literacy of users are not good, therefore, this study explores strategies for improving user health information literacy and interaction quality, by revisting the results and conclusions, from the perspective of social cognition theory (for RQ4). The results and conclusions of this study, on the one hand, promote the deepening development of user Web health information behavior research and the improvement of relevant knowledge system, and also provide reference for the promotion of future research; on the other hand, the proposed strategies and mechanisms provide methods and practical guidance for the design and optimization of personalized information search function of health information system, as well as improving of users' health information literacy and interaction quality. Finally, the future research is prospected according to the limitations and shortcomings of current research. There are 105 figures, 156 tables and 404 references.  

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