Deep learning for lung X-ray image recognition has emerged as a prominent research area. The challenge lies in the small， complexly shaped lesion areas within lung X-rays， where the boundary between the lesion and normal tissue is often unclear， complicating feature extraction in pneumonia images.This paper introduces a Dual Res-Transformer pneumonia recognition model focused on feature enhancement. It incorporates three feature enhancement strategies to augment the model's feature extraction capabilities. The model's key components include： the Group Attention Dual Residual Module （GADRM）， which leverages a dual-residual structure for effective feature fusion and enhances local feature extraction through channel shuffle， channel attention， and spatial attention； the Global-Local Feature Extraction Module （GLFEM）， which applies at the network's higher levels， merging CNN and Transformer benefits to extract comprehensive global and local image features， thereby boosting the network's semantic feature extraction； and the Cross-layer Dual Attention Feature Fusion Module （CDAFFM）， designed to merge shallow network spatial information with deep network channel information， enhancing the network's cross-layer feature extraction.The model's efficacy was validated through ablation and comparative experiments on the COVID-19 CHEST X-RAY dataset. Results demonstrate the network's high performance， with accuracy， precision， recall， F1 score， and AUC values of 98.41%， 94.42%， 94.20%， 94.26%， and 99.65%， respectively.This model offers significant assistance to radiologists in diagnosing various pneumonia cases using chest X-rays， marking a crucial advancement in computer-aided pneumonia diagnosis.