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The present "shallow classification model" have shortcomings on modeling and representation ability, feature extraction, classification performance and so on. This study aims to improve the typical LeNet-5 convolution neural network and obtain three kinds of CNN structures to realize the classification of large and small vehicles. Firstly, we extracted the MFCC feature of vehicle acoustic signals; then took the feature signals as training samples; lastly adjusted the study rate, convolution kernel size and quantity in accordance with experiment and obtained the results. The experimental results indicate that the improved CNN model is better than the traditional machine learning method; and the classification performance of the improved CNN model is improved with the increase of data volume, and the accuracy of the test samples is 96.8%. Keywords—intelligent transportation; vehicle classification recognition; vehicle acoustic signal; feature extraction; deep learning; convolution neural network

Research on Vehicle Classification and Recognition Method Based on Vehicle Acoustic Signal CNN Analysis