In recent years, a new strain of malware, known as mining malware, has emerged, resulting in significant losses for its victims due to its encryption techniques. This research proposes an innovative approach to detect mining malware by integrating hierarchical threat intelligence features, focusing on the static attributes of mining malware. From the perspective of mining malware threat intelligence, we train classifiers using distinct layers of features, including byte-level features, Portable Executable (PE) structure features, and mining operation execution features. By leveraging diverse malicious software attributes for detection preferences, we develop a comprehensive mining malware detector based on a hierarchical feature integration methodology. To evaluate our approach, we conducted experiments in both controlled lab settings and real-world datasets. The results indicate that our hierarchical feature-integrated mining malware detection method achieves an impressive accuracy rate of 97.01% on the simulated real-world dataset, demonstrating a substantial 6.13% accuracy improvement compared to the baseline mining malware detection method.