Time series data come from a wide range of social fields， from meteorology to finance and to medicine. Accurate long-term prediction is a key issue in time series data analysis， processing， and research. Aiming at exploitation and utilization of the correlation of different scales in time series data， a multi-scale information fusion time series long-term forecasting model based on neural network — ScaleNN was proposed with the purpose of better handling multi-scale problem in time series data to achieve more accurate long-term forecast. Firstly， fully connected neural network and convolutional neural network were combined to extract both global and local information effectively， and the two were aggregated for prediction. Then， by introducing a compression mechanism in the global information representation module， longer sequence input was accepted with a lighter structure， which increased perceptual range of the model and improved the model’s performance. Numerous experimental results demonstrate that ScaleNN outperforms the current excellent model in this field — PatchTST （Patch Time Series Transformer） on multiple real-world datasets. In specific， the running time is shortened by 35% with only 19% parameters required. It can be seen that ScaleNN can be applied to time series prediction problems in various fields widely， providing a foundation for forecasting in areas such as traffic flow prediction and weather forecasting.