The invention discloses a movie recommendation method based on feedback of users' various behaviors. The method includes the steps of s1, clustering movies, to be specific, subjecting movie information to feature selection to obtain keyword description of each movie; s2, calculating user similarity, to be specific, clustering users into a plurality of user sets by means of content clustering-behaviors based on the fuzzy theory, with each user having different membership degrees in the different user sets, performing modeling with movie description information and feedback data of users' various behaviors, calculating membership degrees of each user in the user sets, and calculating user similarity according to the membership degrees of the users in the different user sets; s3, generating recommendations, to be specific, generating different movie recommendation lists for the users according to the acquired user similarity. The method helps solve the problem of data sparsity, solve the problem of information loss occurring in traditional 'implicit-explicit' conversion and improve recommendation precision.