The prevention and control of biological invasions have always been a focal issue for governments worldwide. However, due to the large number, wide distribution, and varied morphologies of invasive species, existing target detection methods struggle to effectively identify and distinguish these organisms. Moreover, traditional prediction methods find it challenging to capture the habits of different invasive species, which poses difficulties in predicting their locations and probabilities of occurrence. To address these challenges, we innovatively combine cubic polynomial regression with logistic functions to develop the “Tick-Tock” strategy, and integrate it with YOLOv5 and Geo-fencing technology. This allows us to identify and predict the growth trends, external characteristics, and geographical locations of invasive species from multiple dimensions. We integrate these methods into a priority recommendation system (PRS) to effectively monitor and provide early warnings for biological invasions. We focus on the case of Vespa Mandarinia (VM) in Washington State to demonstrate the superiority of our approach.