The invention provides a game recommending method and system based on user portrait behavior analysis. A feature acquirer is constructed to process user portrait data, application list data and client-reported data to obtain regular feature vectors meeting mathematical modeling requirements; various basic recommending models are used to make predictions to generate a primary user application recommendation list and corresponding download probabilities; a final application recommendation list is generated by combining the download probabilities and an actual label training fusion model. User historical behavior logs are subjected to multidimensional analysis to perform feature extraction so as to construct a user portrait data warehouse. A long and short-term memory network is introduced to the basic recommending models to learn time-series relationships of user behaviors, users' degrees of preference for objects are better depicted, and recommended game applications match well with the needs of users. Integrated learning is added to perform model fusion, learning results of the models are integrated, and accordingly, the stability and generalization ability of a recommending algorithm are improved.