As a critical load-bearing component of bridges, suspension cables are prone to corrosion under environmental exposure during service. The geometric morphology of corrosion pits, particularly those caused by pitting corrosion, induces stress concentration that directly impacts the safe operation of in-service bridges. Therefore, developing an accurate method for fitting the morphology of corrosion pits on steel wires is a prerequisite for conducting refined fatigue life analysis of suspension cables. The geometry of corrosion pits is complex and irregular. Traditional methods that approximate pits with regular geometric shapes fail to account for stress concentrations in locally irregular areas, while directly using the original point cloud data, though preserving complete geometric information, entails excessively large data volumes. To efficiently and accurately assess the geometric morphology of corrosion pits, this study employed a super-depth microscope to collect point cloud data of corrosion pits. The spherical harmonic function was introduced to characterize their three-dimensional morphology of corrosion pits, simplify the dataset, and achieve geometric reconstruction. An ABAQUS finite element model was established and compared with models based on real pit data and traditional ellipsoid fitting to validate the effectiveness of the simplification and reconstruction method. The results show that using a 20 th-order spherical harmonics function can effectively simplify the corrosion pits, reducing the storage space of the corrosion pit dataset by an average of 99.47% while retaining the geometric information with high fidelity. With fatigue life as the evaluation metric, the average error is 0.80%. This study provides a theoretical foundation for the construction of mesoscale models of corrosion pits and the simplification of corrosion pit datasets.