Most existing air quality prediction methods focus on simple time series data for trend prediction， and ignore the pollutant transport and diffusion laws and corresponding classified pattern features. In order to solve the above problem， a PM2.5 diffusion characteristic extraction method based on Candlestick Pattern Matching （CPM） was proposed. Firstly， the basic periodic candlestick charts from a large number of historical PM2.5 sequences were generated by using the convolution idea of Convolutional Neural Network （CNN）. Then， the concentration patterns of different candlestick chart feature vectors were clustered and analyzed by using the distance formula. Finally， combining the unique advantages of CNN in image recognition， a hybrid model integrating graphical features and time series features sequences was formed， and the trend reversal that would be caused by candlestick charts with reversal signals was judged. Experimental results on the monitoring time series dataset of Guilin air quality online monitoring stations show that compared with the VGG （Visual Geometry Group）-based method which uses the single time series data， the accuracy of the CPM-based method is improved by 1.9 percentage points. It can be seen that the CPM-based method can effectively extract the trend features of PM2.5 and be used for predicting the periodic change of pollutant concentration in the future.
