For the dimensional disaster and feature redundancy problems in the process of data mining, a reinforcement learning based feature selection algorithm, which is combined Q learning methods with traditional feature selection methods, is proposed in this study. In the proposed method, the agent acquires a subset of characteristics autonomously through training and learning. Experimental results show that the proposed algorithm can effectively reduce the number of features and has higher classification performance.