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The distribution information of irrigation networks in irrigation area acquired quickly and accurately had great important research significance, especially in the scientific allocation of regional agricultural water resources and improvement of water resources utilization rate. The semantic segmentation model based on fully convolutional networks (FCN) was used to extract the irrigation networks contours. Firstly, the orthophotos collected by UAV were manually labeled. Based on the VGG-19 network, the FCN-8s structure was realized by multi-scale feature fusion, and the Tensorflow deep learning framework was used to construct the FCN irrigation networks extraction model. Secondly，the data sets were enhanced and segmented. Lastly, the data sets were put into the FCN model for training and testing. The experimental results showed that for the test areas with different complexities, the extraction precision, completion and accuracy of the FCN model were 95.78%, 92.29% and 89.45%, respectively, which were higher than the support vector machine (SVM) method and the revised Hough transform (RHT) method. The results showed that the method can achieve high-accuracy extraction of the irrigation networks contours in irrigation area, and had good generalization and robustness, which provided good technical support for further accurate irrigation in agriculture.