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Mikania micrantha is one of the top ten harmful weeds in the world, and its flooding will have a great impact on the ecosystem. Establishing a high spatial resolution and global scale early warning and assessment method for Mikania micrantha is one of the key measures to control Mikania micrantha. At present, Mikania micrantha is mainly monitored by manual survey and satellite remote sensing, but the former is inefficient and the latter is not accurate enough. Unmanned aerial vehicle (UAV) was used as the carrier to collect Mikania micrantha color images in the area to be monitored, the Otsu-K-means, RGB, HSV color space threshold segmentation algorithm and K-means-RGB, K-means-HSV, K-means-RGB-HSV fusion algorithm and MobileNetV3 deep learning algorithm were used for recognition. The recognition results were evaluated by three evaluation indexes: recall rate, accuracy rate and average F1-score value. The experimental results showed that K-means-RGB-HSV algorithm had the best overall recognition effect on Mikania micrantha in full bloom. On this basis, based on the recognition results, an early warning evaluation system of Mikania micrantha was constructed by applying fuzzy analytic hierarchy process and coverage formula, and five Mikania micrantha invasion hazard grades were divided. According to the different monitoring accuracies, grids with different sizes and radiation radius were set, and the accurate distribution heat map of Mikania micrantha invasion was drawn, which could clearly and accurately reflect the harm degree of Mikania micrantha invasion in different areas. Accurate monitoring of Mikania micrantha in full bloom based on UAV remote sensing was achieved with centimeter-level resolution accuracy, which provided strong support for monitoring, early warning and accurate prevention of Mikania micrantha invasion.