In recent years, deep learning of convolutional networks has achieved great success in the fields of image processing and target detection. It has become a new research hotspot to apply Convolutional Neural Networks(CNN) to traditional video compression standards. An improved High Efficiency Video Coding(HEVC) compression algorithm integrated with convolutional neural network is proposed, which integrates down-sampling, HEVC codec, up-sampling and quality enhancement process. In order to extract the structural features of video frames efficiently, two convolutional neural networks are integrated in the proposed compression algorithm. Down Sampling CNN(DwSCNN) replaces bicubic down-sampling, which preserves the detailed information while reducing the resolution, obtaining a more compact low resolution video sequence. The low-resolution video sequence is further compressed by HEVC intra coding, and a quality-enhanced Post Processing CNN(PPCNN) is proposed to improve the degraded video sequence that is restored to the original resolution after decoding. The experimental results show that the proposed compression improvement algorithm can achieve better quality reconstruction than the standard HEVC in the low code rate segment, and can save time by 39.46% and bit rate by 11.04% when the PSNR value is close to the same. The video compression performance of the algorithm is superior to the HEVC standard algorithm and other related literature methods.