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.

Symmetric cryptography is the core technology of data confidentiality in information systems. At the same time， nonlinear S-box is usually the key cryptographic component， and is widely used in the design of block cipher， stream cipher， MAC （Message Authentication Code） algorithm， etc. In order to ensure the security of the cryptographic algorithm design， firstly， the criteria testing methods for differential uniformity， nonlinearity， fixed point number， algebraic degree and item number， algebraic immunity， avalanche characteristic and diffusion characteristic were researched. Secondly， the results of each security criterion of the S-box were designed and output in the visual window， and the detailed descriptions of the corresponding security criterion were given in a pop-up window way. Thirdly， the design of the sub-components of nonlinearity and algebraic immunity was focused， and the linear distribution table was simplified according to the nonlinearity. At the same time， based on the theorem， the calculation process of algebraic immunity was optimized and illustrated with an example. Finally， the S-box testing tool was implemented with seven security criteria， and the test cases were demonstrated. The proposed tool is mainly used to test the security criteria of the nonlinear component S-box in the symmetric cryptographic algorithm， and then provides a guarantee for the security of the overall algorithm.

Concerning the problems of low accuracy, limitations of stagnation and slow convergence speed in the later evolution process of Artificial Fish Swarm Algorithm (AFSA), a Parallel Dynamic weigh Niches Artificial Fish Swarm (PDN-AFS) algorithm based on multi-core cluster was proposed. Firstly, the advantages and disadvantages of AFSA were analyzed, and dynamic weighting factor strategy and niche mechanism were adopted, hence a new Dynamic weigh Niches Artificial Fish Swarm (DN-AFS) algorithm was put forward. Then parallel design and analysis of DN-AFS algorithm based on parallel programming model (MPI+OpenMP) were introduced. Finally, the simulation experiments on multi-core cluster environment were given. The experimental results show that PDN-AFS can effectively improve the convergence speed and optimization performance of the complex multimodal function optimization problem, and achieve high speed ratio.