In order to recommend matters related to government services and improve user efficiency and government service level, a recommendation algorithm is proposed, that is, a collaborative filtering recommendation method for government services combined with user characteristics. Unlike traditional collaborative filtering which does not consider user attributes, this method combines user portrait technology with it. First, the method establishes a user portrait of government services, and then uses the singular value metric analysis method to integrate the user portrait and the user-based collaborative filtering algorithm, so that the feature attributes can participate in the similarity calculation, improve the similarity between users, and solve the problem of data sparsity. To make the results more practical, the method calculates the predicted government service score, and recommends the TOP-N with the highest score to the user. In the experimental part, the actual data of the government affairs service of a city’s enterprise legal person is used for verification. The results show that the algorithm can meet the personalized requirements of the government affairs service recommendations and improve the prediction accuracy.