The Northwest Pacific is one of the regions with high frequency of typhoons in the world. Every year, China is affected by landings of typhoons, which causes severe weather such as wind, rain and storm surge in coastal areas. It is of great significance to accurately predict the typhoon path in northwest Pacific region for disaster prevention and mitigation in China. The multi-component time series prediction model based on recurrent neural network takes the multi-component time series data including the best typhoon track dataset of China Meteorological Administration (CMA) and the European Centre for Medium Range Weather Forecasts (ERA5) as samples. Training three kinds of recurrent neural networks to predict the center location of tropical cyclones (TC) in the future 6, 12, and 24 hours, The results show that when the prediction failure is 24 hours, the E value predicted by long short term memory( LSTM )model is 173.15 km, and the error is much smaller than that of gated recurrent unit(GRU )model and recurrent neural network(RNN) model. It is considered that it is feasible and valuable to predict the location of TC center in the next 24 hours by using LSTM network.