Smart cities depend on information and communication technology to sense and analyze the key information in the core system of urban operation. It needs to make timely and effective intelligent response to urban security threats and common cases. In order to improve the effectiveness and accuracy of identifying common urban cases, this study proposes an automatic identification algorithm for common urban violations. The improved convolution neural network extracts image features, and BP neural network is used for evaluation. On the VOC data set, the algorithm is compared with YOLO and SSD in performance. The results show that the mAP of the improved convolutional neural network can reach 76.5%, and the accuracy of identifying various types of cases is more than 72%, and the accuracy of identifying “graffiti and posted advertisement” is 83.4%. The image recognition technology of cases in a smart city developed in this study can enhance the efficiency of case processing, save human and material resources, and thus can be used to assist urban management, supervision, and administrative law enforcement.