With the rapid development of Internet of things (IoT) technology and the increasing popularity of IoT devices, more and more computing intensive IoT applications came into being. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Further, in order to be subject to a single cloud computing service provider, multi-cloud computing will become an IoT service cloud computing solution. In view of the complexity of multi-cloud scheduling, the application of artificial intelligence will be an important technology to solve the multi-cloud scheduling of IoT. The corresponding talent training plays an important role in the development and implementation of the artificial intelligence multi-cloud scheduling of IoT. Firstly, this paper studies the key influencing factors of IoT’s artificial intelligence multi-cloud scheduling applied talents training. Combined with the characteristics of the development of China’s artificial intelligence industry, this paper summarizes the influencing factors from the four dimensional training path of government departments, universities, enterprises and scientific research institutes. According to the training purpose of artificial intelligence multi-cloud scheduling applied talents training, build an artificial intelligence multi-cloud scheduling applied talents training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors, and calculate the influence degree, affected degree, center degree and cause degree of the factors; Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority respectively, a group of confrontation level topological maps with comprehensive influence values reflecting the interaction of factors are obtained, and relevant suggestions are put forward in order to provide reference for the training of artificial intelligence multi-cloud scheduling applied talents.