Objective:To construct an optimal planning model based on backpropagation(BP)neural network algorithm,and to explore its application value in the quality control of magnetic resonance imaging(MRI)equipment.Methods:Taking image quality,quality control cost and troubleshooting time as control objectives,and 13 indicators such as environmental factors,human factors,equipment factors,and use frequency as decision factors,an optimal planning model based on BP neural network algorithm is constructed.The operation data of a 1.5T magnetic resonance device in clinical use in Zhongshan People's Hospital from 31 May 2021 to 4 June 2023 were selected.The equipment operation data for 52 weeks from 31 May 2021 to 29 May 2022 was used for model training,which was used as the data of the conventional quality control scheme,and the optimal scheme evolution and dynamic optimization were carried out by reverse calculation.The dynamic optimization scheme was used to apply the practice from 6 June 2022 to 4 June 2023,and its operation data was used as the data of the optimization quality control scheme.The equipment image quality score,quality control cost and troubleshooting time of the two schemes were compared.Results:The image quality score of MRI equipment optimized using the optimal planning model for quality control scheme was(4.15±0.35)points,which was higher than that of conventional quality control scheme,the quality control cost and troubleshooting time were CNY(5247.44±1711.39)and(4.34±2.31)h,respectively,which were lower than those of conventional quality control scheme,the differences were statistically significant(t=4.084,6.442,10.776,P＜0.05).Conclusion:The optimal planning model was constructed based on the BP neural network algorithm and the quality control scheme of MRI equipment was optimized,which can effectively improve the quality management level of MRI equipment,ensure image quality,improve equipment stability,reduce failure rates and quality control costs.