In order to solve the problems of low precision of agricultural irrigation and insufficient intelligence of irrigation decision-making in arid areas, an inversion model of soil moisture content was constructed based on multi-spectral image and broad learning, and data enhancement technology was introduced to improve the reliability of the model. On this basis, the water deficit rate was calculated by using the inversion results of multi-scale soil moisture content, and the spatial differential irrigation prescription map was generated. Then, a fuzzy controller with water deficit rate and effective accumulated temperature as double input parameters was established, and a precision irrigation decision-making model based on deep fusion of multi-spectral and fuzzy control was constructed. The results showed that the broad learning system combined with data enhancement technology significantly improved the performance of the inversion model. The determination coefficient of the original modeling set was 0.8596, the root mean square error (RMSE) was 0.0378g/g, and the average absolute error (MAE) was 0.0175g/g. The determination coefficient of the test set was 0.8258, the RMSE was 0.0458g/g, and the MAE was 0.0181g/g. After data augmentation, the test set R2 p was increased by 8.1% , and the test set RMSE was decreased by 47.6% , MAE was decreased by 7.7% . The precision irrigation decision model realized the dynamic optimization of irrigation amount by integrating the fuzzy controller constructed by water deficit rate and accumulated temperature parameters. Pearson correlation analysis showed that there was a significant positive correlation between water deficit rate and irrigation amount during the whole growth period, and the Pearson correlation coefficient was higher than 0.82, which verified the rationality and space-time adaptability of the rule base. At the same time, the error propagation sensitivity analysis revealed that the precision irrigation model had intelligent hierarchical response characteristics. In the high water deficit area, the sensitivity was significantly reduced, which drived the irrigation decision to form an anti-disturbance area, and resisted the risk of water stress through continuous high-order irrigation. In the low water deficit area, the sensitivity enhancement triggered the dynamic fine-tuning of irrigation amount to achieve accurate optimization of resources. The results of irrigation decision-making showed that the irrigation amount output by the model changed regularly with the increase of accumulated temperature. The average irrigation amount was increased from 1.8mm in the early growth period to 3.8mm in the vigorous growth period, and the proportion of medium irrigation strategies was increased from 1% to 77.9% , and significant spatial variability was maintained in each period. The results can provide theoretical support for the optimal design of precision irrigation and intelligent irrigation systems in arid areas.