Abstract: In this study, we employed a combination of genome mining and heteronuclear single quantum coherence (HSQC)-based small molecule accurate recognition technology (SMART) technology to search for fernane-type triterpenoids. Initially, potential endophytic fungi were identified through genome mining. Subsequently, fine fractions containing various fernane-type triterpenoids were selected using HSQC data collection and SMART prediction. These triterpenoids were then obtained through targeted isolation and identification. Finally, their antifungal activity was evaluated. As a result, three fernane-type triterpenoids, including two novel compounds, along with two new sesquiterpenes and four known compounds were isolated from one potential strain, Diaporthe discoidispora. Their structures were elucidated through analysis of high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) and nuclear magnetic resonance (NMR) spectroscopic data. The absolute configurations were determined using single-crystal X-ray diffraction analysis and electron capture detector (ECD) analysis. Compound 3 exhibited moderate antifungal activity against Candida albicans CMCC 98001 and Aspergillus niger.