Although the use of heterogeneous computing systems can accelerate the processing of neural network parameters， it also increases system power consumption significantly. Good power consumption prediction methods are fundamental for optimizing power consumption in heterogeneous systems and handling multi-type workloads. Based on the above， by improving multi-layer perceptron and attention model， a power consumption prediction algorithm was proposed for CPU/GPU heterogeneous computing systems with multi-type workloads. Firstly， considering server power consumption and system features， a workload power consumption model based on features was established. Then， to address the issue that the existing power consumption prediction algorithms cannot solve long-range dependence between system features and system power consumption， an improved power consumption prediction algorithm based on multi-layer perceptron-attention model was proposed， namely Prophet. In the algorithm， the multi-layer perceptron was modified to extract system features at different moments， and the attention mechanism was employed to synthesize these features， so that the long-range dependency problem between system features and power consumption was solved effectively. Finally， the experiments were conducted on real heterogeneous systems， and the proposed algorithm was compared with the power consumption prediction algorithms such as MLSTM_PM （Power consumption Model based on Multi-layer Long Short-Term Memory） and ENN_PM （Power consumption Model based on Elman Neural Network）. Experimental results show that Prophet achieves higher prediction accuracy， reducing the Mean Relative Error （MRE） for workloads blk， memtest， and busspd by 1.22， 1.01， and 0.93 percentage points， respectively， compared to MLSTM_PM， and has low complexity， indicating the proposed algorithm’s effectiveness and feasibility.