Abstract: Red tide is one of marine disasters. It often causes great harm to fishery production and human life. Therefore, it is necessary to strengthen the early warning and forecast of red tide. However because the formation of red tide is very complex, it is very difficult to predict red tide. At home and abroad, there have been a lot of reports about the prediction and forecast of red tide. Different scholars have discussed the reasons for the formation of red tide using different research methods. In this study, 219 red tides data were collected in Fujian sea area from 2000 to 2016. The nonlinear relationship between the 5 meteorological factors, such as temperature, precipitation, wind speed, air pressure and sunshine, was established by using the BP neural network model. First of all, the total collected data of red tide and the corresponding meteorological data were divided into 3 sea areas data called Eastern, Central and South Fujian sea areas, according to their geographical locations, then the three groups of data were input into the model for it to learn and train. The results show that: 1) the 53 training samples in eastern Fujian sea area gave 45 correct predictions, the correct rate was 84.91%, and the 3 simulated prediction samples in the same area were all correct. 2) in 69 training samples of central Fujian sea area, 58 predictions were correct, the accuracy rate was 84.06%, and the 4 simulation predictions were all correct. 3) in 85 training samples in south Fujian sea area, 63 prediction results were correct, and the correct rate was 74.12%, and the 5 simulation samples were all correct. All the expected prediction results achieved the desired goals. Therefore, it is feasible to predict the occurrence of red tide based on the BP neural network model, which can provide a new way to forecast the red tide.