Ferromagnetic materials are widely applied in production equipment in various fields. However, during the manufacturing and operation processes, they are easily affected by mechanical loads and environmental media, and defects such as cracks and corrosion are likely to be generated, which threaten the safe operation of industrial equipment. Based on this, the defect quantification method of three-axis information fusion was carried out to provide technical support for the classification and inversion of different defects by ACFM (alternating current field measurement) technique. Based on the principle of the ACFM technique, simulation models of alternating current magnetic fields for different defects were established. The relationship between the three-axis magnetic flux signals of crack and corrosion defects and the corresponding defect sizes was analyzed, and the forward and inverse laws were determined. Meanwhile, an experimental platform was designed and built, the data volume was expanded, and a three-axis information fusion database was established. With the help of neural networks, the automatic classification and quantification research of crack and corrosion defects was carried out. The results show that there is a significant correlation between the three-axis magnetic flux signals of crack and corrosion defects and the defect sizes. A quantification model that accurately reflects this relationship has been successfully constructed. During the neural network training, the automatic classification accuracy of the model for crack and corrosion defects reaches 100%, and the defect quantification inversion accuracy is above 94%. The defect quantification method of three-axis information fusion based on the ACFM technique in this research is feasible and effective. It can accurately automatically classify and quantify the crack and corrosion defects in ferromagnetic materials, provides a reliable technical means for the detection of defects in ferromagnetic materials in industrial equipment, and helps to improve the safety and stability of the operation of industrial equipment.