Abstract: Geomagnetic storms represent an important type of geomagnetic field disturbance that can cause interference and damage to fields such as communication, power supply, and aerospace technology. Therefore, the advancement and innovation of geomagnetic storm recognition technology have good development prospects for strengthening the application of geomagnetic storm data in related fields. In this study, we leveraged an extensive dataset comprising minute value recordings of horizontal components sourced from 12 permanent geomagnetic observation stations from 2010 to 2023. Employing spectral imaging technology, we conducted a comprehensive artificial intelligence-based image classification analysis to differentiate between geomagnetic storm days and geomagnetic quiet days, utilizing the VGG19 convolutional neural network model. We have obtained good experimental results. This experiment uses accuracy, precision, and recall as evaluation metrics. We have obtained good experimental results. The experimental model demonstrated the accuracy rate of 97.41%, with a precision value of 98.00% and a recall rate standing at 96.80%. These indicators collectively emphasize the reliable predictive ability of our model. Furthermore, the application of spectrograms within the context of image recognition and classification has demonstrated significant feasibility. Notably, the VGG19 convolutional neural network model exhibited remarkable feasibility when tasked with categorizing geomagnetic storm days and geomagnetic quiet days. The recognition accuracy of this model for geomagnetic storm days is relatively high and the model itself is relatively stable. However, there is some fluctuation in the recognition of geomagnetic quiet days, which also means that the model still has room for further improvement, especially by increasing the number of training sets and improving the learning accuracy of the model for map information. In summary, our research findings contribute to the improvement of geomagnetic storm identification methods, providing a promising approach to enhance geomagnetic storm prediction and monitoring capabilities, and ultimately promoting the wider application of geomagnetic storm information in related fields.