A popularity prediction method of Twitter topics based on evolution patterns was proposed to address the problem that the differences between evolution patterns and the time?effectiveness of prediction were not taken into account in previous popularity prediction methods. Firstly， the K?SC （K?Spectral Centroid） algorithm was used to cluster the popularity sequences of a large number of historical topics， and 6 evolution patterns were obtained. Then， a Fully Connected Network （FCN） was trained as the prediction model by using historical topic data of each evolution pattern. Finally， in order to select the prediction model for the topic to be predicted， Amplitude?Alignment Dynamic Time Warping （AADTW） algorithm was proposed to calculate the similarity between the known popularity sequence of the topic to be predicted and each evolution pattern， and the prediction model of the evolution pattern with the highest similarity was selected to predict the popularity. In the task of predicting the popularity of the next 5 hours based on the known popularity of the first 20 hours， the Mean Absolute Percentage Error （MAPE） of the prediction results of the proposed method was reduced by 58.2% and 31.0% respectively， compared with those of the Auto?Regressive Integrated Moving Average （ARIMA） method and method using a single fully connected network. Experimental results show that the model group based on the evolution patterns can predict the popularity of Twitter topic more accurately than single model.