Abstract: When recommending Web service resources based on user behavior characteristics， insufficient consideration of the correlation between different characteristics makes the low F-Measure value of the recommendation method. Therefore， a personalized recommendation method of Web service resources based on maximum entropy is proposed. According to the historical operation records of users， the implicit behavior characteristics of users are extracted from three aspects of user characteristics， commodity characteristics and interaction characteristics to improve the missing information of users. Collaborative filtering method is used to mine the association between users and resources and generate user interest matrix. Based on the principle of maximum entropy calculation， the feature function is constructed to analyze the correlation between features， and based on this， the Web service resource selection algorithm is designed. Finally， the constraints are established according to the basic attributes of users and the resource scoring matrix， and the optimal personalized resource recommendation results are obtained. The experimental results show that compared with the recommendation method based on ontology reasoning and intelligent computing， the F-measure value is increased by 41 percentage points and 33 percentage points， and the resource recommendation results can better meet the needs of users.

Key words: maximum entropy, Web service resources, personalized recommendation, user interest, implicit behavior, feature extraction