Mobile crowdsensing is one of the core basic technologies in the digital construction of smart cities, and it is a hot research topic in the field of mobile computing. In recent years, although there have been many representative research results on mobile crowdsensing, there is still a long way to go before it is widely used on a large scale, and it still faces the problem of low user participation in the actual promotion and application. To this end, the social network influence maximization (IM) transmission model is introduced. It considers the lack of probabilistic information in reality and learns the probability of influence while performing influence activities through online learning, or in other words, the influence model information is constantly updated according to the user feedback, so as to propose a new task diffusion scheme based on the model. Through experiments with real social network data sets, the results show that the proposed method is more effective than the traditional IM method in terms of transmission scope, and it makes a contribution to the practical promotion and application of mobile crowdsensing systems.