In the field of MOFs research, searching for novel MOFs is still a complicated problem. After MOFs are processed by “material genetic encoding”, the Genetic Algorithm (GA) can be used to rapidly explore novel MOFs, but their performance depends on the setting of individual fitness functions, and the effective evaluation of the novel MOFs also contributes to the effectiveness of this method. As one of the representative methods of machine learning, the Artificial Neural Network (ANN) can uncover the non-linear constitutive relationships. In this paper, the neural network is introduced to predict the adsorption capacity for CH₄ gas by the novel MOFs generated by GA, thereby facilitating the search for novel MOFs by GA. The experimental results show that the neural network can thoroughly evaluate the novel MOFs materials, demonstrating the feasibility of combining the neural network and GA for the search and screening of the novel MOFs.