Generally, when individuals obtain information about epidemic risks, they will arouse their awareness of self-protection and thus take various effective self-protection measures independently. However, there is often significant different response for different individual. To explore more comprehensively the impact of differences in individuals' ability to identify or accept external information on awareness diffusion, as well as on epidemic spreading, this paper constructs an awareness-epidemic double-layer network with higher-order interactions, innovatively introduces an individual heterogeneity factor, and proposes the UAU-SIS （ Unaware- Aware- Unaware- Susceptible- Infected- Susceptible ） awareness-epidemic spreading model. The heterogeneity in this model is mainly reflected by individuals' first-order degree, second-order degree, coordination factor, and response factor. Based on the Microscopic Markov Chain Approach (Microscopic Markov Chain Approach， MMCA) and the proposed UAU-SIS model, this paper conducts a theoretical analysis of the coevolution of awareness and epidemic, and theoretically deduces the mathematical expression of the epidemic threshold. Monte Carlo（ Monte Carlo， MC） numerical simulations verify the feasibility and effectiveness of the MMCA theoretical analysis. Meanwhile, numerous numerical simulations have explored the impact of individual heterogeneity on awareness diffusion, epidemic spreading, and epidemic threshold. The results show that reasonable regulation of the first-order average degree and second-order average degree of the awareness layer can effectively promote awareness diffusion and improve the overall effect of epidemic prevention and control. In addition, reducing the coordination factor or increasing the response factor can effectively promote awareness diffusion, raise the epidemic threshold, and thus block epidemic spreading.