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The analysis of the attitudes of online users plays an important role in real life and the future society, which can help us to analyze and predict the behavior of the users. The researchers also carried out a lot of related work. However, most of the previous classic methods searched the rules of the structure of the graph or the propagation path and did not consider the internal psychological dynamic mechanism of the user. In the context of big data, identifying online behavioral motivations of users through online information should be a future development trend. In this paper, we delve into the users’ internal psychological energy based on the Elaboration Likelihood Model and divide the user’s behavioral drive into the central route and the peripheral route. The degree of user’s processing of the known information and elaboration determines which route is more effective. The central route is mainly the user’s behavioral habits studied through user role theory. In the peripheral route, it calculates the influence of other users through a kind of hydromechanics algorithm—FluidRating. The final model we called ELMFluid which can express the flow of psychological energy through thermodynamics and hydromechanics. The experiments are performed using two real datasets in the end. The results show that the new model is superior to other algorithms.

User’s Attitude under the Perspective of Mental Energy Flow