为提高恶意软件检测模型的性能和可解释性，提出一种基于数据预处理与模型优化的智能融合检测方法。该方法集成多种机器学习算法，通过随机森林算法提取关键特征，并采用常春藤优化算法（ivy optimizer algorithm, IVYA）进行参数调优，构建融合模型以提升检测效果。模型利用SHAP（shapley additive explanations）方法进行解释性分析，增强检测结果的透明度与可靠性。实验表明，该方法在多个数据集上准确率、精确率、召回率等指标均超过99%，相较于传统方法表现出明显优势，为网络安全提供了更强的防护手段和更高的可解释性。

In order to improve the performance and interpretability of malware detection models, an intelligent fusion detection method based on data preprocessing and model optimization has been proposed. Multiple machine learning algorithms were integrated in this method, with key features being extracted using the random forest algorithm. Parameter tuning was performed with the ivy optimizer algorithm (IVYA) to construct a fusion model that enhances detection effectiveness. The SHAP(shapley additive explanations) method was utilized for interpretability analysis, thereby improving the transparency and reliability of the detection results. Experimental results show that accuracy, precision, recall, and other metrics exceed 98% across multiple datasets, demonstrating a significant advantage over traditional methods. Stronger protection and higher interpretability for cybersecurity are provided by this approach.