In the realm of cybersecurity, malware adopts various evasion tactics, such as obfuscation and code rewriting, to evade detection by network security protection systems, perpetually evolving in sophistication. This presents a formidable challenge for traditional signature-based and machine learning-based malware detection methods, rendering them ineffective against emerging malware variants. To mitigate this problem, security analysts strive to detect malware variant samples at the earliest possible stage. This typically involves labor-intensive manual analysis, where analysts meticulously scrutinize network activities, system calls, and other suspicious behaviors. However, given the vast volume of potentially malicious samples identified daily by network security systems, discerning the ‘high-value’ samples worthy of further investigation becomes a daunting task. To address this challenge, we propose a novel malware variant detection method rooted in knowledge transfer and ensemble learning. This method aims to detect and identify malware variant samples, streamlining the process of screening out highly suspected files for security analysts. By doing so, it alleviates the burden of manual analysis and judgment. This study leveraged the Microsoft Kaggle dataset to simulate real-world scenarios of malware variant detection. The results demonstrate a recall rate of 61.9% for malware variant samples, with an accuracy rate of 56.52%. Notably, security analysts can pinpoint a malware variant sample by analyzing an average of 1.77 samples, significantly reducing the manual analysis workload. These experimental findings underscore the effectiveness and robustness of our proposed method.