Mobile Crowd Sensing is a human-based data collection model, and the approach taken to recommend data collection tasks to users in order to maximize task acceptance rates is an important part of this research. Existing task recommendation methods are based only on intuitive data for unconscious analysis and decision-making, and lack the embodiment of cognitive intelligence. To address the above problem, a conscious task recommendation based on cognitive reasoning computing in Mobile Crowd Sensing has been proposed, using knowledge from cognitive science to simulate the human thinking process in order to achieve warm learning and conscious recommendation of sensing tasks. First, the task attributes are segmented into positive and negative attributes using a Kernel Density Estimation method based on bandwidth self-selection. Then, the user's attribute preferences are diagnosed by the Cognitive Diagnostic Method to obtain the user's preference vector. Finally, get the overall preference trend of users based on the Drift Diffusion Model, and make decisions according to whether the current task drift direction is consistent with the user preference trend. Simulation experiments were conducted using the Taobao dataset, MTurk dataset, and synthetic dataset, it was ultimately proven that conscious task recommendation combined with user cognitive ability effectively reduced RMSE and improved task acceptance rate. RMSE was 10.5%∼70.8% lower than other methods, and the task acceptance rate was basically over 80%, with most of the results being over 90%.