In view of the problems of ambiguous and poor effect of vehicle image segmentation methods in actual traffic scenarios, this paper proposes a MSSA-UNet model that integrates multi-scale modules and spatial attention mechanism based on the UNet neural network model. In the encoding and decoding stage, dilated convolution is used to build a multi-scale module to improve the limited receptive field size of the convolutional layer while the output contains multi-scale feature information. Before up-sampling, a spatial attention mechanism is introduced to compensate for the problem of local information loss during the sampling process and improve the feature restoration ability. Combined with cross entropy loss and Dice loss, the network learning and training process is optimized, and the segmentation accuracy of the model is improved. The experimental results show that the MSSA-UNet model proposed in this paper achieves 83.48% in the IoU evaluation index for vehicle image segmentation tasks, which is 2.28% higher than the accuracy before improvement, the predicted value of the model is closer to the real value, and the segmentation effect is better, which effectively improves the segmentation performance of the model.