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Eco-environmental quality assessment is an important process for ensuring sustainable regional development because it encourages the construction of an ecological civilization. This assessment also lays a foundation for informing digital management and decision-making for the ecological environment in the near future. Due to the particularity of mountainous areas, its Eco-environmental quality assessment needs to be deeply specific and may require re-working. Here, the AHP-Entropy model was developed and constructed to scientifically analyze and evaluate the Eco-environmental quality in mountainous Western Sichuan in 2009 and 2019 at the township (street) unit. It is based on indicators representing Biological richness, Vegetation cover, Water network density, Land stress, and Social-economic stress. This newly developed model offers a more complete assessment, is based on more easily acquired data and has a higher accuracy than past models. The Eco-environmental quality index (EQI) representing eco-environmental quality (EEQ) has five levels of rating: Better, Good, Moderate, Bad and Worse. After applying this model to the region, the results show that: 1) The ecological environmental quality in the Western Sichuan mountains has improved over the past 10 years. 2) The eco-environment in the study area is generally Good, with small areas receiving a rating of Moderate. 3) The areas considered Better are mainly distributed on Longmen Mountain, Daliang mountain and Qionglai mountain, while the Moderate areas are mainly distributed in the western Chengdu Plain, the Panzhihua urban area, and Shaluli mountain. 4) The areas rated Better earn their rating due to high vegetation coverage, high habitat quality, and low degree of land degradation, such as land desertification and soil erosion. The areas with moderate eco-environmental quality were mainly affected by frequent human activity or serious land degradation. This comprehensive regional Eco-environmental quality assessment can also be used in other areas with mountains.