Cellulose enzymatic saccharification is a key link in converting lignocellulose to fuel ethanol via a sugar platform. In this paper, the maximum information coefficient (MIC) algorithm is proposed to detect linear correlations and nonlinear correlations between bioinformatics variables from the application of bioinformatics. Meanwhile, to address the problems of excessive grid division and low statistical efficacy of MIC algorithm, BackMIC, an optimization algorithm using quiz control number of segments, is proposed to terminate the network search using the chi-square test. Then, cellulase hydrolysis and adsorption experiments were designed and performed, and the experimental data were analyzed based on the BackMIC algorithm. The optimum activity temperature of different cellulases was around 50°C, while the hydrolysis rate of cellulases of different treated raw materials increased and then decreased with the increase of temperature, and basically, the maximum was achieved in the temperature range of 50~55°C. In the pH test, the optimum temperature of cellulase hydrolysis was concentrated at 4.4~4.5. In the cellulase adsorption experiments, the enzymatic activity of steam-popped wheatgrass was adsorbed in the following order: FPA> C1> CMCase> β-Case. The study of cellulase hydrolysis based on bioinformatics reveals the synergistic relationship between cellulase hydrolysis and adsorbed properties, which provides a theoretical basis for reducing the ineffective adsorption of cellulose and the development of enzymatic strengthening technology. The theoretical basis for reducing the ineffective adsorption of cellulose and the development of enzymatic enhancement technology can help reduce the cost of lignocellulose enzymatic digestion.