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 PM_2.5 diffusion characteristic extraction method based on Candlestick Pattern Matching （CPM） was proposed. Firstly， the basic periodic candlestick charts from a large number of historical PM_2.5 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 PM_2.5 and be used for predicting the periodic change of pollutant concentration in the future.

In view of the lack of training data for Vietnamese scene text detection and the incomplete detection of Vietnamese tone marks in the detection， a text detection algorithm for Vietnamese scenes based on a modified instance segmentation method Mask R-CNN was proposed. In order to segment Vietnamese scene text with tone marks accurately， only P2 feature layer was utilized to segment the text area， and the mask matrix size of the text area was adjusted from 14 × 14 to 14 × 28 to adapt the shape of most texts. Aiming at the problem that duplicate text detection boxes cannot be eliminated by the conventional Non-Maximum Suppression （NMS） algorithm， a filter module for the text areas named Text region filtering branch was designed and added after the detection module to effectively eliminate duplicate detection boxes. A model joint training method was used to train the network. The training process consists of two parts： the first part is the training of the Feature Pyramid Network （FPN） and the Region Proposal Network （RPN） of the model， which used large-scale open Latin text data for training to enhance the generalization ability of the model to detect text in different scenes； the second part is the training of the candidate box coordinate regression module and the segmentation module named Box branch and Mask branch， which used pixel-level labelled Vietnamese scene text data for training to enable the model to segment the Vietnamese text area including tone marks. Many cross-validation experiments and comparison experiments verify that the proposed algorithm has better precision and recall under different Intersection over Union （IoU） thresholds compared with Mask R-CNN.