The majority of machine learning algorithms that have been applied in data-driven predictive mapping of mineral prospectivity require a sufficient number of training samples (known mineral deposits) to obtain results with high performance and reliability. Semi-supervised learning can take advantage of the huge amount of unlabeled data to benefit the supervised learning tasks and hence provide a suitable scheme for mapping mineral prospectivity in cases where only few known mineral deposits are available. Semi-supervised random forest was used in this study to map mineral prospectivity in the southwestern Fujian metallogenic belt of China, where there is still excellent potential for mineral exploration due to the large proportion of areas covered by forest. The findings obtained from the current study include: (1) semi-supervised learning can make use of both the labeled and unlabeled samples to help improve the performance of mapping mineral prospectivity; (2) multi-dimensional scaling can be used to explore the clustering structure within the samples, which provides a tool to validate the usability of semi-supervised learning algorithms. In addition, the prospectivity map obtained in this study can be used to guide further mineral exploration in the southwestern Fujian of China.