In order to realize more accurate mangrove extraction and monitoring， four mangrove plantation areas， including the Aojiang River coast in Wenzhou City， were taken as the study area， and the distribution of Mangrove forests was extracted and accuracy verified based on the DeepLabV3+ semantic segmentation model using Sentinel-2 remotely sensed imagery data， which was applied to analyze the spatial change of mangrove forests in the period of 2019~2023. The results show that： ① the Mangrove information extraction model constructed by DeepLabV3+ network can better distinguish Mangrove and Non-Mangrove areas， with fewer mis- and omissions； ②the semantic segmentation algorithms are significantly better than traditional machine learning methods， with the DeepLabV3+ method having the highest accuracy， with an precision of 84.89% and a Kappa coefficient of 0.82； ③The growth of Mangrove forests is greatly affected by the geographical location and growth environment， and Mangrove forests in the intertidal zone of the coast or at the mouth of the sea are more susceptible to the influence of typhoons， tides， etc.， and the encroachment of mangrove forests' growth space by exotic species， such as spartina alterniflora， etc.， are all the key factors that cause the low survival rate of Mangrove seedlings and the slow growth rate. Therefore， the semantic segmentation model based on DeepLabV3+ can better recognize and extract the Mangrove forests and provide data base support for the monitoring and assessment of Mangrove forests in Wenzhou City.