Deep reinforcement learning easily leads to unsafe actions in the training process due to its trial-and-error learning characteristics in decision-making problem of autonomous lane changing. Therefore， a safe reinforcement learning method for decision making of autonomous lane changing based on trajectory prediction was proposed. Firstly， the future trajectories of the vehicles were predicted through probabilistic modeling of maximum likelihood estimation. Secondly， driving risk assessment was performed by using the obtained trajectory prediction and safety distance. And the safe actions were constrained according to the driving risk assessment results， which means that the action space was cut into the safe action space and the intelligent vehicle was guided to avoid dangerous actions. The proposed method was tested and compared with Deep Q-Network （DQN） and its improved methods in the freeway scene of simulation platform. Experimental results show that the proposed method can reduce the number of collisions by 47%-57% compared to other methods while ensuring fast convergence during intelligent vehicle training process， and thus improves the safety during training process effectively.