Disclosed is a wind power prediction method based on a time sequence and neural network method. The wind power prediction method comprises the following specific steps: step 1, establishing a time sequence model; step 2: establishing a neural network model, and initializing a neural network; step 3, establishing a wind speed prediction model: performing data differential processing according to original data of a wind speed, and establishing a wind speed prediction model based on a time sequence method; step 4, predicting wind power according to the wind speed prediction model and a wind speed-power conversion relationship; and step 5, establishing a wind power prediction model: establishing a prediction model of wind power according to the wind speed prediction model and the wind speed-power conversion relationship, and obtaining a wind power prediction value by using a wind speed prediction value, obtained by using the wind speed prediction model, as an input value of the wind power prediction model. In this manner, prediction errors caused by a nonlinear relationship between the wind speed and the wind power are reduced effectively; and the method is suitable for short-term prediction of wind power.