With the popularity of the Internet, people's lives are becoming more and more convenient. However, the network security problems are becoming increasingly serious. This paper, aiming to better protect users’ network security from the internal and external malicious attacks, briefly introduces the probabilistic neural network and principal component analysis method, and combines them for detection of network intrusion data. Simulation analysis of Probabilistic Neural Network (PNN) and Principal Component Analysis-Probabilistic Neural Network (PCA-PNN) are carried out in MATLAB software. The results suggest that the Principal Component Analysis (PCA) algorithm greatly reduce the dimension of the original data and the amount of calculation. Compared with PNN, PCA-PNN has higher accuracy and precision rate, lower false alarm rate, and faster detecting speed. Moreover, PCA-PNN has better detecting performance when there are few training samples. In summary, PCA-PNN can be used for the detection of network intrusion threat.

For a long time, China's medical problem is very serious. There are very few researches on medical waste data, which can not provide enough evidence for managers. Therefore, combining some methods of data analysis and data mining to analyze the medical waste data.In this study, based on the collection of the hospital garbage data of over five years from some area in China, the hospital garbage data are analyzed with consideration of the location, the hospital level, hospital beds and number of doctors and staff members, by using some data analysis and data mining methods. The time series analysis of garbage data proves that the medical wastes so produced are on the rise and the sharing of the burden of medical missions is unbalanced with regard to the hospital location and levels. By establishing an auto regressive integrated moving average（ARIMA） (0,1,1) model, the prediction and analysis for the every-day production of the hospital wastes in the area are made.The research results of the K-Means clustering analysis and the PARETO contribution analysis provide some empirical evidences for the future planning and development of the hospitals in this area

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