In complex pattern recognition scenarios, multi-source information fusion faces critical challenges in handling conflicting evidence. This paper proposes an innovative conflict evidence combination rule based on a multi-dimensional weighted evidence optimization method to address this issue. First, systematic indicator classification and coefficient of variation analysis are conducted to rigorously select key conflict evaluation metrics. This approach effectively addresses the issues of incomplete evaluation dimensions and limited indicator selection observed in prior research. Subsequently, the multi-dimensional weighted evidence optimization method is employed to calculate the relative importance of each piece of evidence. On this basis, conflicting evidence is effectively discounted to reduce conflict and uncertainty among the evidence. Finally, the Dempster combination rule is applied to fuse the discounted conflict evidence for decision-making, achieving more accurate and robust judgments. Furthermore, this paper constructs a decision-level multi-source information fusion model. The model integrates the outputs of three basic classifiers—K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forests (RF)—as multi-source information, and applies the proposed conflict evidence combination rule for fusion. This approach fully leverages the advantages of different classifiers, thereby enhancing the accuracy and robustness of pattern recognition. To verify the effectiveness and practical application potential of the proposed method, this paper conducts empirical analysis on multiple sets of experiments. The experimental results demonstrate that, compared with existing methods, the proposed method exhibits significant advantages in accuracy and robustness, proving its potential application value in addressing pattern recognition problems in complex scenarios.