Urban traffic time series pattern recognition is mainly based on two methods: Distance calculation and feature extraction. The recognition results based on distance calculation are affected by the non-linear features of the time series, and it is also difficult to extract multi-time period Shapelet sub-sequences based on the feature extraction algorithm. Therefore, in order to accurately identify the data pattern of urban traffic index, a method for pattern recognition of traffic time series data based on convolutional neural network(CNN) was proposed. Firstly, the time series data are pre-processed and transformed into N
-dimensional matrix to determine the input data of the neural network model. Then, the characteristic information of the input layer is extracted through repeated training of the characteristic network, and the pattern type of the traffic index is determined. Finally, Softmax classifier is used to classify the modes and realize the classification of traffic index. The time series pattern recognition of Beijing's traffic index from 2016 to 2018 was taken as an example. Compared with the traditional machine learning algorithm based on distance calculation, it was found that the CNN can more accurately divide the time series data of traffic index into five types of patterns that are consistent with the real situation. The experimental results show that the CNN is more accurate and effective for pattern recognition of time series data, and is suitable for correlation research and pattern discovery of time-phase change data.