Existing malware detection methods based on dynamic API sequence mining do not consider the behavior differences between different types of malware, resulting in low efficiency of malicious code detection. In this paper, an objectoriented association mining technology is introduced, and a malware detection method is proposed based on the longest frequent sequence mining algorithm of the same category. First, the method extracts the dynamic API sequences of sample files and preprocesses them; then, the longest frequent sequence mining algorithm is used to mine the longest frequent sequence sets of multiple categories; finally, the longest frequent sequence set is used to construct the word bag model, according to the word bag model, the dynamic API sequences of sample files are transformed into vectors, so that the longest frequent sequence mining algorithm can be used to mine the longest frequent sequence sets of multiple categories. Random forest algorithm is used to construct classifier to detect malicious code. In this paper, we use the data set provided by the Aliyun Security Algorithms Challenge. The accuracy rate and AUC of malware detection are 95.6% and 0.99, respectively. The results show that the proposed method can effectively detect the malware.