A super-resolution reconstruction algorithm based on convolutional neural network (CNN) is proposed to solve the problem that the traditional depth map super-resolution reconstruction algorithm needs to extract the feature manually and the computational complexity is higher, and it is not easy to get the proper representation feature. CNN does not need to extract the specific features from the image in advance for the specific task, but the simulated human vision system can extract the feature independently by hierarchical abstraction process on the original depth map. This algorithm can achieve mapping learning directly from the low resolution depth map to high resolution depth map. The mapping is implemented by seven convolution layers and one deconvolution layer. The convolution operation is used to learn the rich image features,and the deconvolution realizes that the upsampling is used to reconstruct the high resolution depth map. The experimental results of the Middlebury RGBD dataset show that the average peak signal-to-noise ratio (PSNR) and root-mean-square error (RMSE) obtained from the model can increase by 2.7235 dB and decrease by 0.098 compared with the traditional bicubic interpolation algorithm, respectively. Compared with the classical image super-resolution reconstruction using deep convolutional neural networks, the performance is also improved with 1.5244 dB of PSNR increment and 0.043 of RMSE decrement.