Time series have attracted widespread attention in many fields today . Based on the analysis of complex networks and visibility graph theory, a new time series forecasting method is proposed . In time series analysis, visibility graph theory transforms time series data into a network model . In the network model, the node similarity index is an important factor . On the basis of directly using the node prediction method with the largest similarity, the node similarity index is used as the weight coefficient to optimize the prediction algorithm . Compared with the single-point sampling node prediction algorithm, the multi-point sampling prediction algorithm can provide more accurate prediction values when the data set is sufficient . According to results of experiments on four real-world representative datasets, the method has more accurate forecasting ability and can provide more accurate forecasts in the field of time series and actual scenes.