Click is one of the external manifestations of the audience's scarce attention resources in the current network communication environment. To get the audience's attention and improve the audience's click through rate, the recommendation algorithm is used in the information push of the network platform. The recommendation algorithm represents the user's interest through the data in the network and provides the user with the content they are interested in to improve the user's click through rate in the platform. However, in psychology, there is a lack of relevant research on the specific relationship between individual interest and click behavior in the network environment. At the same time, the role of interest in the specific psychological process of individual click behavior is not clear. Based on the above, this paper starts from the interest, tries to analyze the relationship between interest and click behavior, and discusses the role of identifying interest in promoting click behavior by elaborating the role of interest in the psychological process of individual click behavior. This paper is mainly divided into three parts, which are explored for the above research content. In Study 1，the relationship between individual interest, topic interest, situational interest and click behavior was tested by questionnaire and scale. It is found that there is a moderate correlation between individual interest and click behavior, and a high correlation between topic interest, situational interest and click behavior. The research on the mediating effect shows that topic interest and situational interest have a greater impact on individual's click intention than individual interest, and in most cases, individual's topic interest mediates the impact of individual interest on click intention. It provides the basis for the follow-up research. In the second study, a method of individual interest recognition based on microblog media is proposed. Through the user's behavior data in social networks, the features that can be used to predict the user's personal interest are obtamed. The modeling and prediction results of the experiment show that the individual
interests can be predicted through the data in social networks. The third study explored the specific mechanism of topic interest and situational interest influencing click behavior through interview method and found the two-stage psychological process behind clicking behavior when browsing online video, which provided a theoretical basis for identifying topic interest and situational interest. The factors that affect the generation of individual topic interest are found: the novelty of information, the comprehensibility of information and the individual's prior knowledge, which provides a theoretical basis for the identification of topic interest and situational interest in the network environment. On this basis, through large-scale data samples to verify these factors, the data shows that in the non-individual interest area, click intention is more affected by the related factors of topic interest, comprehensibility, and new heterosexuality.
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