The analysis of geomagnetic suitability is the basis and premise of geomagnetic matching navigation and positioning. A geomagnetic suitability evaluation model using mixed sampling and an improved back propagation neural network (BPNN) based on the gray wolf optimization (GWO) algorithm by incorporating the dimension learning-based hunting (DLH) search strategy algorithm was proposed in this paper to accurately assess the geomagnetic suitability. Compared with the traditional geomagnetic suitability evaluation model, its generalization ability and accuracy were better improved. Firstly, the key indicators and matching labels used for geomagnetic suitability evaluation were analyzed, and an evaluation system was established. Then, a mixed sampling method based on the synthetic minority over-sampling technique (SMOTE) and Tomek Links was employed to extend the original dataset and construct a new dataset. Next, the dataset was divided into a training set and a test set, according to 7:3. The geomagnetic standard deviation, kurtosis coefficient, skewness coefficient, geomagnetic information entropy, geomagnetic roughness, variance of geomagnetic roughness, and correlation coefficient were used as input indicators and put into the DLH-GWO-BPNN model for model training with matching labels as output. Accuracy, recall, the ROC curve, and the AUC value were taken as evaluation indexes. Finally, PSO (Particle Swarm Optimization)-BPNN, WOA (Whale Optimization Algorithm)-BPNN, GA (Genetic Algorithm)-BPNN, and GWO-BPNN algorithms were selected as compared methods to verify the predictable ability of the DLH-GWO-BPNN. The accuracy ranking of the five models on the test set was as follows: PSO-BPNN (80.95 %) = WOA-BPNN (80.95%) < GA-BPNN (85.71%) = GWO-BPNN (85.71%) < DLH-GWO-BPNN (95.24%). The results indicate that the DLH-GWO-BPNN model can be used as a reliable method for underground geomagnetic suitability research, which can be applied to the research of geomagnetic matching navigation.