**5. Conclusions**

This study analyzed different numbers of hidden layer nodes and found that when the number of hidden layer nodes was five, the minimum *MSE* was 0.00080796, indicating that the model performed well. The results indicated that backpropagation neural networks could predict the threshing performance of the flexible threshing device with an acceptable degree of accuracy (*R* = 0.980, *RMSE* = 0.138, *MAE* = 0.153). The built neural network model prediction predicted the performance of the flexible threshing device well. The regression determination coefficient R<sup>2</sup> between the predicted and experimental data was 0.953, indicating that the predicted data of the built neural network model was in good agreement with the experimental data. The ANN method is an effective method for predicting the threshing performance of flexible threshing devices in rice. The established artificial neural network model exhibited stable prediction of the threshing performance of the flexible threshing device during operation. The sensitivity analysis revealed that RS, TC, and SC are important factors affecting the performance of the flexible threshing device, with an average relative importance of 15.00%, 14.89%, and 14.32%, respectively. FQ had the least impact on threshing performance, with an average threshing relative importance of 11.65%. These results can guide the optimal design of flexible threshing cylinders and improve the performance of the flexible threshing device.

**Author Contributions:** Conceptualization, L.M. and F.X.; methodology, L.M.; software, L.M.; validation, L.M., F.X. and D.L.; formal analysis, L.M.; investigation, D.L.; resources, X.W. and Z.Z.; data curation, X.W. and D.L.; writing—original draft preparation, L.M.; writing—review and editing, L.M., F.X. and Z.Z.; visualization, L.M.; supervision, F.X.; project administration, D.L.; funding acquisition, F.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Hunan High-Tech Industry Technology Leading Plan Project (Science and Technology Research category) (2020NK2002); Hunan Agricultural Machinery Equipment and Technology Innovation Research and Development Project (Xiangcai Agricultural Index (2021) No.47); and Hunan Agricultural Machinery Equipment and Technology Innovation Research and Development Project (Xiangcai Agricultural Index [2020] No.107).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.
