The present invention discloses a method for reconstructing dynamic PET concentration distribution image of dual-tracer based on stacked autoencoder. The invention introduces deep learning into dynamic tracer PET concentration distribution image reconstruction, and the process is mainly divided into two stages of training and reconstruction. In the training phase, train multiple autoencoders using the concentration distribution images of mixed tracers taken as input, and the concentration distribution images of the two tracers taken as labels to build the stacked autoencoder. In the reconstruction phase, the concentration distribution images of the individual tracer can be reconstructed by inputting the concentration distribution images of the mixed traces to the well trained stacked autoencoder. The present invention realizes the reconstruction of the dynamic PET concentration distribution images of mixed tracers from the data-driven point of view, and effectively solves the problems of poor reconstruction effect and inability of simultaneous injection.