The structure and parameters of convolutional neural network (CNN) determines the performance of image classification. Aiming at the problems of complex structure and a parameters that require a lot of manual settings in deep network, a CNN image classification algorithm based on the evolution of densely connected networks(D-ECNN) is proposed in this work. The algorithm can effectively search the network structure space, and realizes the adaptive optimization of deep network structure and parameters based on limited computing resources. The classification experiment results on the vehicle data set show that the accuracy of this algorithm can reach more than 95%, which is about 1% higher than that of the visual geometry group (VGG16) algorithm. The model file of this algorithm is smaller and the test speed is faster.