With the features of low Signal-to-Noise Ratio （SNR） and blurred edges， ultrasound images of the brachial plexus nerve are hard to be segmented manually. Although some results have been gained by existing segmentation models， the segmentation effect is not satisfied due to the small target area and irregular shape of the brachial plexus nerve structure. Aiming at the above problems， a multi-scale feature fusion-based brachial plexus nerve segmentation model was proposed， namely Nerve-segmentation Feature Pyramid Network （Ner-FPN）. In the feature extraction stage， an Xception-like structure was designed for multi-scale feature extraction. In the prediction segmentation stage， a bidirectional FPN structure was used for feature fusion prediction. The BP （Brachial Plexus） dataset from the Kaggle brachial plexus nerve ultrasound image segmentation competition was used as the experimental data. The experimental results show that compared with the mainstream deep learning segmentation models U-Net and SegNet （Segmentation Network），the Dice Similar Coefficient （DSC） of Ner-FPN model for brachial plexus nerve segmentation can reach 0.703， which is 10.7 percentage points and 14.5 percentage points higher than those of U-Net and SegNet， and 5.5 percentage points and 3.4 percentage points higher than those of improved models QU-Net and Efficient+U-Net in the same dataset， verifying that the proposed model can be an aid for diagnosis.