The rapid development of the Internet of Things and multimedia applications has led to an exponential growth in mobile network traffic year by year. In order to meet the demand for large amounts of data transmission and solve the problem of insufficient spectrum resources, millimeter waves are adopted for 5G communication. For B5G/6G, effective use of spectrum resources is one of the key technologies for the development of mobile communications. Therefore, this study uses a lightweight neural network to predict cellular traffic based on regional data, considering the data types of temporal and spatial dependence at the same time. Furthermore, in order to optimize the prediction performance and reduce the number of parameters of the neural network, this study uses a meta-heuristic algorithm to adjust the hyperparameters and combines local and global explanations to interpret the improvement of traffic prediction. The local explanations show the adjustment results of a single hyperparameter, and global explanations show the correlation between different hyperparameters and their influence on the amount and accuracy of model parameters. The simulation results show that compared with adjustment strategies of the manual method and greedy algorithm, the proposed explainable learning method can effectively improve the accuracy of cellular traffic prediction, reduce the number of parameters and provide a reasonable explanation.