Multi-label text classification aims to assign several predefined labels or categories to text. To fully explore the correlations among labels, current methods typically utilize a label relation graph and integrate it with graph neural networks to obtain the representations of label features. However, such methods often overly rely on the initial graph construction, overlooking the inherent label correlations in the current text. Consequently, classification results heavily depend on the statistics of datasets and may overlook label-related information within the text. Therefore, this study proposes an algorithm for multi-label text classification based on feature-fused dynamic graph networks. It designs dynamic graphs to model label correlations within the current text and integrates feature fusion with graph neural networks to form label representations based on the current text, thus achieving more accurate multi-label text classifications. Experimental results on three datasets demonstrate the effectiveness and feasibility of the proposed model as it shows excellent performance in multi-label text classifications.