Text in natural scenes exhibits diverse scales and morphologies, which is particularly pronounced in Chinese text. This cross-scale characteristic limits feature propagation in existing cross-scale fusion methods and tends to overlook the capture of detailed features. This paper proposes FDB, a natural scene Chinese text detection model based on DBNet, incorporating Feature Pyramid Enhancement Module with Attention Mechanism. Through a collaborative mechanism of cross-scale feature fusion and channel attention, the model effectively enhances its cross-scale feature integration capability and improves Chinese text feature capture. To address the lack of Chinese scene adaptability in existing datasets, this study specifically constructs a Chinese Text Dataset for model training. Experimental results on public benchmarks demonstrate that the FDB model achieves enhanced validity in Chinese text detection under complex backgrounds, while achieving an improvement in F1-score.