In traditional event-related potential (ERP) research, due to the varying latencies and mechanisms of different components, studies typically focus on specific features for signal extraction and analysis, which limits the exploration of their interactions and hinders the understanding of the brain′s overall mechanism in processing visual stimuli. To address this issue, this study selects P1, N170 and P3 as target ERP components and proposes a dynamic weight assignment-based ERP feature extraction and fusion strategy. First, time-frequency domain processing is applied to ensure consistency in dimensionality across feature segments. Next, the random forest algorithm was used to evaluate the Gini importance of each component for each participant under different experimental paradigms, followed by dynamic weight assignment to the feature segments, resulting in a multi-component fused feature. Finally, convolutional neural networks are employed to classify the fused data, verifying the significant classification effect of the fused features. Experimental results show that, for the Face Perception N170 and Active Visual Oddball P3 datasets, classification accuracies reached 96.4% and 92.4%, respectively, with improvements of 11.6% and 10.2% over non-fused features. It improved by 8.4% and 5.3% compared to equal-weight fusion, and was more effective than the fusion features with attention mechanism, proving that the weighted feature fusion method proposed in this paper can enhance the relevant component signals and improve classification accuracy, providing new insights into the overall process of visual stimulus processing in the human brain.