Lower-limb activity recognition (LLAR), which is driven by wearable sensors, has recently become a popular research topic in human assistive devices. Although multimodal information fusion is crucial for improving the accuracy of LLAR, traditional feature-level fusion strategies cannot directly model the relationship between multimodal features according to the lower-limb activity patterns and they realize fusion based on the relationship. To address these limitations, a dynamic graph topology generation (DGTG) mechanism that uses a graph topology to model the relationship between multimodal features mined by a convolutional neural network (CNN) is proposed in this study. A weighted graph isomorphism network (WGIN) that can realize multimodal feature fusion based on the relationship (i.e., graph topology) is also proposed, resulting in a novel feature-level multimodal information fusion framework called CNN-DGTG-WGIN. This proposed method achieved the best average accuracies of 96.3 and 99.7% for two publicly available datasets. This method integrates the determination of the graph topology into an end-to-end learning process without requiring any prior knowledge or original graph structures. Further, CNN-DGTG-WGIN effectively incorporates both linear and nonlinear correlations between features during the DGTG process. Analysis results revealed that the graph topology (multimodal feature relationship) learned by CNN-DGTG-WGIN was closely related to lower-limb activity patterns, encoding relevant information about these patterns, which is the essence of its superior performance.