Drone guide line recognition is an automatic path finding method based on ground guide lines. To address the issues of slow recognition speed and low recognition accuracy of ground guide lines， a multi-task lightweight model with data fusion called Mobile-FuUnet based on U-shaped Network （U-Net） was proposed. Firstly， on the structure of U-Net， MobileNet-V3 was introduced for feature extraction， and Depthwise Separable Convolution （DSC） was introduced to reduce the number of model parameters， so as to establish a multi-task lightweight model. Finally， based on attention mechanism for data fusion， the polynomial feature matrix of the pre-image was introduced to solve the computational problem caused by the large area missing at the edge of the guide line， in order to improve the operational accuracy of the model. Multiple comparisons were carried out on Tusimple dataset and the drone guide line dataset. Experimental results show that on the drone guide line dataset， Mobile-FuUnet model can achieve guide line recognition task with the frame rate of 109 frame/s， the Mean Intersection over Union （MIoU） of 98.71%， the F1 score of 99.64 %， and the curve model R2 score of 95.03%. Compared with models such as U-Net， ENet， and DeepLab v3， the proposed model improves both running speed and computational accuracy.