Aiming at the problem that traditional methods can not effectively predict multi-noise and nonlinear time series, focusing on multi-scale features fusion, a prediction method based on tree structure Long Short-Term Memory (LSTM) neural network was proposed and verified. First of all, the core methods of realizing the prediction were proposed, and the internal advantages of the methods were analyzed. Secondly, the prediction model based on tree structure LSTM neural network was constructed. Finally, the model was verified based on the international gold spot transaction data of the last ten years. The results show that the prediction accuracy is nearly 10 percentage points higher than the minimum success rate, and the availability of the methods is proved.