A method for personalized recommendation of film and television resources in a social network environment, belonging to the technical field of data analysis and push, comprising the following steps: S1: online comment acquisition and preprocessing, S2: online comment sentiment value calculation, and S3: movie viewing decision criteria and weights OK, S4: Sorting of film and television resources: Combining the probability language decision matrix, value function, and weight function to obtain movie x i The comprehensive prospect value of , the larger the comprehensive prospect value, the more worthy of recommendation, and the higher the ranking. The present invention fully considers the influence of irrational factors such as the viewer's psychological behavior on the viewer's decision-making while considering various objective factors, so that the recommendation of film and television resources is more realistic and accurate.