Aiming at the problems of low accuracy and low operation efficiency of federated learning algorithm in user behavior prediction, a loss-free Federated Learning Security tree (FLSectree) algorithm was proposed. Firstly, through the derivation of the loss function, its first partial derivative and second partial derivative were proved to be sensitive data, and the optimal split point after encryption was returned by scanning and splitting the feature index sequence, so as to protect the sensitive data from being disclosed. Then, by updating the instance space, the splitting was continued and the next best split point was found until the termination condition was satisfied. Finally, the results of training were used to obtain local algorithm parameters for each participant. Experimental results show that the FLSectree algorithm can effectively improve the accuracy and the training efficiency of user behavior prediction algorithm under the premise of protecting the data privacy. Compared with the SecureBoost algorithm in Federated AI Technology Enabler (FATE) framework of federated learning, FLSectree algorithm has the user behavior prediction accuracy increased by 9.09% and has the operation time reduced by 87.42%, and the training results are consistent with centralized Xgboost algorithm.