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The acquisition process of airborne geophysical remote sensing data is affected by external factors such as electromagnetic wave radiation, resulting in low classification accuracy of airborne geophysical remote sensing data. Therefore, a classification method for airborne geophysical remote sensing data based on self coding neural network is proposed. Set classification standards for remote sensing data based on the basic characteristics of aerial geophysical exploration objects. Complete the preprocessing of airborne geophysical remote sensing data through radiation correction, geometric correction, noise elimination, and other steps. Construct a self coded neural network, use self coded neural network algorithms to extract features of remote sensing data from aspects such as spectrum, shape, texture, and determine the type of airborne geophysical remote sensing data through feature matching. Through classification performance testing experiments, it is concluded that the proposed method has an average classification success rate and error rate of 99.8% and 0.6% for global remote sensing data, and an average classification success rate and error rate of 99.8% and 0.3% for local remote sensing data, respectively, indicating that the proposed method has significant advantages in classification performance.