Abstract: ‍ ‍In order to improve the anti-clutter and interference ability of vehicle mounted millimeter wave radar in complex urban road environment， this paper uses convolutional neural network （CNN） feature parameter extraction and target classification performance， and proposes an improved method based on CNN-based vehicle millimeter-wave radar target detection method. This method uses sliding window to segment the range Doppler two-dimensional data of millimeter wave radar echo signal， and uses CNN network model to process the segmented two-dimensional matrix to train the two-dimensional CNN network model and its parameters， so that it has the ability to extract the echo features and classify the target based on the feature parameter model. Implement the target detection function. Then， by optimizing the structure of the convolutional neural network model， adding a batch normalization layer， optimizing the Dropout layer to inactivate the low-weight features， and adaptively deleting some neural nodes to modify the nonlinear activation function of this layer， the false alarm probability of the CNN model target detection can be further reduced. The experimental results show that， under the condition of the same false alarm probability， the target discovery probability of the CNN network detection method is better than the traditional unit average constant false alarm detection method， and it can still maintain a high detection probability under the condition of low signal-to-noise ratio； At the same level of discovery probability， the false alarm probability of the modified CNN network detection method can be increased by about 1 order of magnitude compared with that before the modification.