An Adjustment of LSTM for Prediction of Time Series with Seasonal Components The gradient disappearance of Long Short-Term Memory (LSTM) network in sequence modeling reduces the accuracy of the model in time series prediction tasks, especially in medium and long-term multi-step prediction, and reduces the attention of the model to the key information in the sequence context. Gradient disappeared the root cause of the memory mechanism lies in the LSTM gating lose control over the layer back propagation gradient in the cycle, so considering adjustment at gating methods of the circle layer structure. And specifically to contain certain elements (such as Seasonal components) training sequence to make improved LSTM model based in sequence prediction tasks have significant attention. In this study, based on the LSTM model, the Seasonal-LSTM (S-SLTM) is improved by adjusting the existing single-branch forgetting gate to a seasonal gate with dual branches, and introducing the range of the input sequence as the pass-selector for dividing the branches. Experimental results show that the prediction accuracy of the single-layer S-LSTM is 28.1% higher than that of the single-layer LSTM in the text binary classification sentiment analysis of English movie reviews IMDB.