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In the application performance management system, the future load situation of the system has important guiding significance for the operation and maintenance scheduling. In the cloud computing environment, the elastic scaling computing capability provides the possibility to adjust the system scale. According to the future load conditions of the system, it can be adjusted in advance: You can expand the cluster before the load is increased to ensure the quality of service; after the load is reduced, If no load is expected to increase in a certain period of time, the scale of the cluster can be reduced in time and the operating cost of the enterprise can be reduced. In the financial field, the ARIMA model is a commonly used time-series prediction model, but its application requires manual intervention to analyze the temporal stability, and the adjustment process is too complicated. In recent years, the development of neural network technology has led to the development of artificially more intelligent technologies. This paper designed and tested the effect of neural network load prediction such as ANN, RNN, GRU, and LSTM. Experimental results show that the LSTM network is accurate and stable in performance and is an ideal model for system load prediction.