A state-of-art deep learning method was proposed to achieve higher accuracy of MPX(multiplexing) estimation. By analyzing the similarity between the MPX results and the real data, it was proved that hardware counts gained by running the same program was linear correlated. By applying the MLP(multilayer perceptron) and Bi-GRU(bidirectional gated recurrent unit) model, the MPX data was fitted. Based on DTW (dynamic time warping), a new metric DTW-cost was proposed to judge the accuracy of MPX result. Experiment results show that when sampling 15 hardware events simultaneously, average result of 13 high performance computing applications gained by the MLP model has a 10.53% higher relative accuracy than the fixed interpolation method. The MLP model has a 19.8% improvement at most. On the hardware events which MLP has a relatively poor performance, the Bi-GRU model improved relative accuracy score by 28.8% on average.