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Unilateral breast reconstruction is different from bilateral breast reconstruction and breast augmentation, as it requires more consideration of the symmetry between the reconstructed breast and the contralateral breast. However, there are currently no implants specifically targeted to unilateral breast reconstruction, nor is there is objective classification criteria for breast appearance type. A total of 212 breasts from 153 patients were measured. Breast measurements, breast surface curvature distributions, and elliptic Fourier coefficients were used to describe breast features. We objectively classified the appearance of breasts using machine learning and used the resulting cluster centers to construct the most adaptive implants for each type of breast. All of the breasts clustered into 4 types. The implants corresponding to each type of breast were constructed using cluster centers. The resulting cluster centers were then used to choose a suitable implant for patients requiring unilateral breast reconstruction. Contour coefficients were used to evaluate the clustering results, with an average score of 0.53. The two breasts that develop normally in the same person were treated as the same class. The score obtained after statistical classification was 0.47. These results demonstrate that our proposed method can improve the classification of breasts of different shapes. This method provides a foundation for improving the symmetry of unilateral breast reconstruction and the construction and selection of implants.