Gene　similarity　mining　through　meta-paths　in　the　heterogeneous　network　composed　of　genes-disease-phenotypes　is　one　of　the　important　methods　in　the　field　of　gene　similarity　search.　Since　the　current　meta-path-based　algorithms　do　not　consider　the　influence　of　autocorrelation　between　disease　and　disease,　and　between　phenotype　and　phenotype　on　gene　similarity,　the　potential　and　implicit　similar　genes　in　the　heterogeneous　network　cannot　be　fully　mined.　To　address　this　issue,　this　paper　first　explores　the　autocorrelation　expressions　of　diseases　and　phenotypes　by　constructing　a　semantic　contribution　graph　and　calculating　the　similarity　of　Gaussian　kernel　topology,　and　then　by　integrating　the　autocorrelation　into　the　heterogeneous　network,　a　fully　linked　network　model　with　correlation　weights　containing　autocorrelation　information　between　diseases　and　phenotypes　is　constructed.　This　network　is　then　utilized　to　calculate　gene　similarity　based　on　the　gSim-Search　principle.　The　proposed　method　makes　full　use　of　autocorrelation　information　between　diseases　and　phenotypes　to　search　for　potentially　similar　genes,　and　indirectly　solves　the　problem　of　sparseness　of　heterogeneous　network　links.　Experiments　show　that　the　proposed　method　improves　the　accuracy　of　prediction　when　calculating　and　ranking　the　similarity　of　the　two　pathogenic　genes　of　breast　cancer　and　obesity.　©　2022　IEEE.