With the popularity of blockchain concepts and the appreciation of cryptocurrencies, an increasing number of hackers and illegal elements have started to focus on stealing users’ hardware resources for mining activities. In this context, web mining has emerged, which can occupy the computing resources of website visitors to mine cryptocurrencies. However, this method can affect user experience, increase energy consumption, and reduce computer performance, making web mining detection and defense increasingly important. Due to the high degree of obfuscation and complex behavior in web mining code, this paper proposes a mining detection model based on machine learning, which extracts features from multiple dimensions and combines machine learning algorithms to identify web pages with mining behavior. We used four classification algorithms for model training, and through extensive experiments and performance comparison, the Random Forest algorithm was proven to be the most effective [1]. This indicates its high accuracy and suitability for detecting malicious mining activities. Our research not only provides a robust method for identifying malicious web mining but also lays a foundation for future cybersecurity research, offering insights into the application of machine learning techniques in this domain.