Abstract In order to enhance the accuracy and robustness of lane line recognition in dynamic and complex environments, this paper proposes a lane line detection model based on a cross-convolutional hybrid attention mechanism (CCHA-Net). Unlike traditional approaches that separately employ channel and spatial attention, our proposed mechanism integrates these modalities through cross-convolution, thereby enabling cross-group feature interaction and dynamic spatial weight allocation. This novel integration not only improves the continuity of elongated lane features but also enhances the model’s ability to capture long-range dependencies in challenging scenarios. Additionally, this paper designs a lightweight message-passing module that employs dual-branch multi-scale convolutions to achieve cross-spatial domain feature fusion while reducing the number of parameters. Experimental results demonstrate that CCHA-Net achieves an F1 score of 80.2% on the CULane dataset and an accuracy of 96.8% on the TuSimple dataset, effectively enhancing lane line recognition accuracy in ever-changing and intricate environments.