Vehicle geomagnetic navigation faces a key issue of magnetic interference affecting navigation accuracy during dynamic driving. Traditional fixed-parameter compensation methods struggle to adapt to interference changes under different motion states. This paper reveals the limitations of a single fixed-parameter compensation model by analyzing the relationship between motion state and magnetic compensation effect, providing a theoretical basis for the development of adaptive compensation methods. The CNN-SRU motion state recognition model constructed in the study achieved a recognition accuracy of 99.61%, with training efficiency improved by 12.8%~28.4% compared to the comparative model, and inference delay reduced by 25.4%~38.5%. Based on the recognition results, the compensation performance of the single ellipsoid fitting model was systematically evaluated, and significant differences in compensation effects under different motion states were found: The standard deviation after compensation was 49.39 nT for uniform linear motion, which exhibited the best performance; the standard deviation after compensation was 533.35 nT for steering motion due to complex interference; and the standard deviation after compensation reached 147.98 nT for acceleration motion due to strong transient interference. The research indicates that motion state is a key factor affecting the magnetic compensation effect, and fixed-parameter models cannot meet the requirements of all operating conditions. The “state recognition-compensation evaluation” framework established in this paper provides theoretical support and technical paths for the development of adaptive magnetic compensation methods.