Aiming at the problem of low multi-task offloading efficiency in the “cloud+edge” hybrid environment composed of “central cloud server and multiple edge servers”， a task offloading method based on probabilistic performance awareness and evolutionary game theory was proposed. Firstly， in a “cloud + edge” hybrid environment composed of “central cloud server and multiple edge servers”， assuming that all the edge servers distributed in it had time-varying volatility performance， the historical performance data of edge cloud servers was probabilistically analyzed by a task offloading method based on probabilistic performance awareness and evolutionary game theory for obtaining the evolutionary game model. Then， an Evolutionary Stability Strategy （ESS） of service offloading was generated to guarantee that each user could offload tasks on the premise of high satisfaction rate. Simulation experiments were carried out based on the cloud edge resource locations dataset and the cloud service performance test dataset， the test and comparison of different methods were carried out on 24 continuous time windows. Experimental results show that， the proposed method is better than traditional task offloading methods such as Greedy algorithm， Genetic Algorithm （GA）， and Nash-based Game algorithm in many performance indexes. Compared with the three comparison methods， the proposed method has the average user satisfaction rate higher by 13.7%， 117.0%， 13.8% respectively， the average offloading time lower by 6.5%， 24.9%， 8.3% respectively， and the average monetary cost lower by 67.9%， 88.7%， 18.0% respectively.