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In order to solve the problems of traditional MOOC teaching resources retrieval methods, such as low retrieval accuracy, poor retrieval recall and low retrieval efficiency, this paper proposes a comprehensive retrieval method of MOOC teaching resources based on eigenvalue extraction. The feature value of MOOC teaching resources is extracted by grey level co-occurrence matrix, and the similarity calculation of resource content features is realised by feature attribute annotation. The MOOC teaching resources search process is designed, and the comprehensive retrieval of MOOC teaching resources is realised by feature value extraction. The experimental results show that the retrieval accuracy of this method is low, the retrieval recall rate is as high as 95%, and the retrieval efficiency of teaching resources is effectively improved.

Software defect is an important metric to evaluate software quality. Too many defects will make the software unavailable and cause economic losses. The aim of software defect prediction (SDP) is to find defects as early as possible. Based on this, source project selection and transfer defect learning based cross-project defect prediction (STCPDP) is proposed. This method firstly sets the threshold of the metrics to predict the defect more effectively, secondly computes the similarity between different project versions to find the appropriate train sets, and finally combines the popular transfer defect learning method TCA + to predict software defects based on the logistic linear regression model. Experimental results show that when the defect probability threshold is about 0.4, STCPDP has better performance based on the F-measure metric, and STCPDP can effectively improve the popular CPDP models.