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As an effective network protection method, computer user behavior anomaly detection can detect unknown attack behaviors. In order to detect user behavior anomalies more efficiently, the authors propose a computer user behavior anomaly detection model based on the K-means algorithm. According to the actual characteristics of single-user behavior, the algorithm uses sliding time window to define transactions and uses the first location strategy to mine behavior patterns. On this basis, the fault-tolerant mode is adopted to compare the current behavior mode with the normal behavior mode, and the anomaly detection results are obtained. Experiments show that using data mining technology, the association rules of user commands, and the mining of sequence patterns, it can effectively discover the user’s behavior pattern, and using sequence matching algorithms such as recursive correlation functions and calculating the similarity between the user’s historical pattern and the current pattern, it provides the possibility to accurately judge user behavior. The following conclusions are obtained through experiments: the model training time is short, the accuracy is high, and it has certain robustness.

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Computer network, as the basic course of teaching information majors in colleges and universities, in the process of teaching and learning, shows the characteristics of rich content, abstract theory, and difficulty understanding. This requires us not only to pay attention to theoretical teaching but also to pay attention to experimental teaching in the study of this subject. The current computer network experiment teaching mainly takes the form of computer room as the teaching location, which consumes a lot of manpower and material resources. That computer network experiment teaching based on simulation has lost the practical significance of network teaching. Through the analysis of the characteristics of the experimental teaching of computer network courses, this study studies and designs a set of computer network experimental platforms based on virtualization, aiming at the deficiencies in the existing experimental teaching of computer network courses. When the thread pool is 1, 2, 3, and 4, the average response time of the system is 324873 ms, 279309 ms, 227300 ms, and 221670 ms, respectively.

In order to control the grassland ecological environment, an application method of multisource data fusion technology in the construction of land ecological index is proposed. Due to the high requirements for grassland environmental monitoring, the use of traditional technologies to monitor grassland environmental conditions lacks certain effectiveness, has high investment costs, and consumes a lot of manpower and material resources. The use of sensors to dynamically monitor the grassland environment is conducive to monitoring the environment from a scientific and technological level. By understanding the fusion principle and process of three fusion methods, adaptive weighted average, BP neural network, and D-S evidence theory, the construction of Bashang grassland ecological energy big data platform based on multisource data fusion is proposed. A two-level data fusion model based on grassland environmental monitoring is proposed. Several environmental parameters in the experimental environment were monitored, and the validity of the two-level fusion model was verified by two evaluation indicators, the mean absolute percentage error and the corrosion error. This suggests that a combination of BP neural network and D-S proof theory improves system performance. It provides the possibility for more comprehensive monitoring of grassland ecological environment in the future.