Aiming at the complicated iterative operations of traditional Compressed Sensing(CS), long reconstruction time and poor quality, a compressed sensing reconstruction algorithm for Non-linear Measurement Convolutional Neural Network(NMECNN) is proposed by combining the deep learning method. This algorithm compresses the overall width and height of the image as a measurement network to replace the traditional random measurement matrix for image reconstruction. At the same time, it uses multiple expanded convolutional layers and upsampling PixelShuffle methods to obtain detailed information of different scales of the image. Through experimental comparison with other documents, the average Peak Signal to Noise Ratio(PSNR) values of this algorithm at different sampling rates are higher than that of MSRNets algorithm by 1 dB, 0.7 dB, 0.82 dB, 1.61 dB, and the Structural Similarity(SSIM) values are higher by 0.03, 0.04, 0.24, 0.10 units. The reconstruction time in the CPU is less than that of the MSRNet algorithm by 0.175 5 s, 0.399 8 s, 0.41 s, 0.396 s, respectively. Through big data sets and noise experiments, it is verified that the image reconstruction quality of this algorithm is significantly improved, the reconstruction time is greatly shortened, and it has a strong ability to resist noise attacks.