Winter jujube fruit recognition is the key technology to realize automatic picking, fruit trees precision management and yield forecast in winter jujube orchard. The rapid and accurate recognition of winter jujube fruits in natural scene affects real-time operability of automatic picking and reliability of monitoring and prediction directly. According to the complex recognition conditions, such as dark light, backlighting, occlusion, and dense fruits in winter jujube orchards, YOLO v3-SE model embedded in SE Net was proposed based on YOLO v3. SE Net adaptively recalibrated channel-wise feature responses by explicitly modelling interdependencies between channels. It strengthened important and valid features, and weakened unimportant and invalid features to improve the performance of feature maps. The deep convolutional neural network built in the article was TensorFlow. After the YOLO v3-SE model was trained and its recognition effect was tested on test samples, and 0.55 was selected as the optimal confidence threshold for the final detection. The P, R, F and mAP were used to assess the differences between YOLO v3-SE and YOLO v3 models. Test results showed that the model proposed got significantly good results. The detection results had the P value of 88.71%, R value of 83.80%, F value of 86.19%, and mAP value of 82.01%. Compared with the results of YOLO v3, the F value and mAP value had an increase of 2.38 percentage points and 4.78 percentage points. Meanwhile, there was no significant difference in detection speed. The further experiments compared the test results of the proposed model and YOLO v3 in complex conditions. In the data sets of backlight and dark-light fruit, the F value and mAP value of the proposed model reached 83.10% and 76.58%. In the data sets of occlusion and dense fruit, the F value and mAP value of the proposed model were 85.02 % and 74.78%. In the data sets of white-ripe, crisp-ripe and full-ripe stage fruit, the F value and mAP value of the proposed model were 86.37%, 89.91%, 91.49%, and 81.18%, 85.15%, 87.49%, respectively. Compared with the original YOLO v3, the F value was increased by 1.75~2.77 percentage points, and the mAP value was increased by 2.38~4.81 percentage points. The detection performance was significantly improved. The above content verified the effectivity of the YOLO v3-SE model. The model proposed can provide a method for winter jujube automatic picking and orchard yield forecast.