To solve the practical problems in vehicle type classification on toll roads, a deep learning network based on dual-view feature fusion, named ETCLNet, was proposed in this paper. A dual-branch architecture was adopted by the network to extract the features of vehicle head and side body images, respectively, and combined with an innovatively designed multi-scale feature extraction module, namely IPFE and an adaptive weighting-based bilinear feature fusion (AWBF) mechanism, efficient and fine-grained vehicle feature representation was achieved. Through the design of parallel multi-scale convolutions and residual connections, the problem of gradient vanishing was effectively alleviated, and the adaptability to complex scenarios was enhanced by the IPFE module. The high-order interaction of features between angles was further optimized by the AWBF mechanism, and efficient fusion of multi-angle features was achieved, and classification performance was significantly improved through adaptive weighting and element-wise multiplication. Experimental results indicate that ETCLNet outperforms existing mainstream models in multiple evaluation metrics (e.g., accuracy, precision, recall, F₁ value, and S_AUC), which solves the problem of the low vehicle type recognition rate of cameras in existing toll road audit systems. In addition, existing hardware devices are fully utilized by this design, which reduces deployment costs. A scientific and efficient solution for multi-view vehicle recognition and smart transportation is provided by this paper, and new ideas for deep learning network design are offered.