DeepCS, a kind of coflow scheduling algorithm, is obtained through reconstruction. The coflow resource view is regarded as the image to be processed later, and the optimal scheduling effect of coflow is achieved according to the previous learning strategy.The feature parameters extracted by DNN need not be designed by manual method, and can be realized by separate learning process.The training input includes various network and task situations, with the motion probability distribution as the output, and EPiSOdE as the unit to carry out the training process.The simulation results are as follows: when the coflow reaches a larger rate, all algorithms will need longer coflow completion time. At this point, the flow time and working pressure of the scheduling algorithm will increase, thus forming a longer average coflow completion time.Under the lower coflow arrival rate, VARYS and DeepCS have similar performance, both of which are better than PFABRiC, and DeepCS has the fastest performance improvement.