To improve the scene perception ability of traditional imaging lidar system and the generalization ability of the signal processing algorithm, a cognitive method of imaging laser radar based on deep learning is proposed. Through the result of deep learning point cloud target detection algorithm, the core imaging parameters are further regulated, and the cognitive feedback is formed, improving the system imaging quality and environmental perception. To test and verify the feasibility of the proposed method, a cognitive imaging laser radar presentation module is designed and implemented. Through the experimental comparison and analysis, three imaging parameters of laser emission power, scanning field of view and scanning angular resolution of imaging system are selected for cognitive feedback, and the module combined with the deep learning method realize the dynamic interaction learning of the scene, which has solved the problem of solidification of traditional lidar imaging parameters. The experimental results show that the cognitive imaging mode based on deep learning can effectively improve the generalization ability and target detection accuracy of the existing deep learning point cloud target detection algorithm.