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In order to obtain the most intuitive pedestrian target detection results and avoid the impact of motion pose uncertainty on real-time detection, a pedestrian target detection system based on convolutional neural network is designed. The CNN computing framework is used as the main body of the hardware structure, the target sensor and the neural type convolutional classifier are connected in a hierarchical manner, and the detection architecture is built according to the parallel detection principle and the convolutional neural architecture. Establish a training file system, configure the necessary detection file parameters by catering to the target training environment, and complete the sample training process of the pedestrian target to be detected. In the detection node architecture, the configuration conditions associated with the access interface are specified, and the additional module multiplexing acceleration structure is used to directly obtain the pedestrian target detection results, and the pedestrian target sample reconstruction is realized, and the pedestrian target detection system based on the convolutional neural network is completed design. Experimental results show that, compared with PCA and SVM algorithms, after applying the convolutional neural network detection system, the pedestrian target detection amount per unit time reaches 9.6×109T, and the target data accumulation rate is reduced to 1.14×109T/s, which can be obtained intuitively Pedestrian target detection results effectively suppress the impact of motion pose uncertainty on real-time detection of the system.