## 4 Conclusions  
From the change in _kNDVI_ in 2019-2024, _kNDVI_ gradually increased, indicating that the growth of vegetation has improved. The average value of _kNDVI_ in the study area in 2019 was 0.4086, and that in 2024 reached 0.4927. The value of _kNDVI_ in 2024 was significantly higher than that in 2019. In general, _kNDVI_ showed an upward trend in the past 6 years, indicating that ecological restoration measures have gradually taken effect. From the perspective of the change trend of vegetation coverage, the overall vegetation coverage of the desertification restoration research area in Xiwuzhumuqin Banner showed a gradual increase trend from 2019 to 2024, and the vegetation coverage increased by 19 percentage points in 2024 compared with 2019. At the same time, the transformation of vegetation coverage from low-level areas to high-level areas in the study area is more prominent. The vegetation coverage in 23.72% of the entire study area remains basically unchanged, and the area of vegetation improvement accounts for 42.17% of the total area of the study area. It is mainly distributed in the northern, southern, and central regions of the study area. The degraded area accounted for 34.11%, and the degraded parts were distributed in all regions, but the degree of degradation was more serious in the southwest and northeast regions. The monitoring model of vegetation change in grassland desertification ecological restoration based on deep learning is constructed to improve the monitoring accuracy of vegetation in grassland desertification area, and the area of vegetation increase or decrease can be clearly marked. By comparing the output results of the analysis model, the _kNDVI_-PT-Former model based on _kNDVI_ constructed in this study performed well in the monitoring of vegetation changes in grassland desertification ecological restoration, and showed excellent performance on the test set. The CPA was 0.7295 and the IoU was 0.7228. At the same time, the field verification of the ecological restoration area was carried out, indicating that the monitoring effect of the model was the best, and the overall monitoring accuracy of the model was improved by 11%. Furthermore, the stability of the model's performance was confirmed through evaluation with five-fold cross-validation. The _kNDVI_-PT-Former model developed in this study enables large-scale, rapid, and more accurate monitoring of vegetation changes in grassland desertification restoration areas, providing technical support for ecological restoration monitoring and assessment of sandy grasslands while offering decision-making references for desertification control and sustainable grassland development. Conceptualization, Z.W. ; methodology, Z.W., T.B. and F.Y. ; software, F.Y. ; validation, F.Y. and Z.W. ; formal analysis, F.Y. ; investigation, F.Y., Y.W., Z.W. and T.B. ; resources, Z.W. ; data curation, Z.W., F.Y., X.Y. and Y.W. ; writing--original draft preparation, F.Y. ; writing--review and editing, F.Y. and Z.W. ; visualization, F.Y. ; supervision, Z.W., Y.Z. and T.B. ; project administration, Z.W., Y.Z. and T.B. ; funding acquisition, Z.W., Y.Z. and T.B. All authors have read and agreed to the published version of the manuscript.