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With the widespread adoption of smartphones and the rapid growth of the mobile Internet, the Android platform has become highly popular. However, its open-source nature has also made it susceptible to rampant malware attacks. In light of this, this research paper proposes a malware detection system based on Smali-LSTM to enhance the efficiency of malware detection on the Android platform. The detection system employs a static analysis approach to extract Smali files from applications. These extracted Smali files undergo a series of preprocessing steps to extract relevant features. To ensure compatibility with the LSTM model, the preprocessed Smali files are fragmented into smaller pieces. The paper explores and tests fragments of various sizes to identify the optimal configuration that yields the best results. The findings of the study demonstrate that the proposed Smali-LSTM model outperforms existing works that utilize the same dataset and LSTM model. The achieved results showcase an accuracy of 96.58\% and an impressive precision of 100\%. These outcomes validate the effectiveness and superiority of the proposed model in accurately detecting malware in Android applications.