Depression　is　a　prominent　public　mental　health　issue　affecting　millions　globally.　With　the　growth　of　online　communities,　many　individuals　suffering　from　depression　are　increasingly　seeking　guidance　and　support　from　community-based　Question-Answering　(QA)　systems.　Previous　methods,　which　recommend　the　best　answer　by　calculating　the　semantic　distance　between　a　question　and　its　potential　answers,　seldom　incorporate　psychological　indicators　as　features.　Answers　from　non-professionals　might　offer　misguided　information　to　those　with　depression.　In　our　approach,　we　employ　a　multi-task　learning　method,　taking　into　account　both　semantic　information　and　psychological　indicators　for　answer　selection　in　depression　community　QA　systems.　To　compute　the　semantic　similarity　between　a　given　question　and　its　potential　answers,　we　gather　surface-layer　textual　information.　We　then　evaluate　these　candidate　answers　using　psychological　indicators　from　two　dimensions:　(1)　employing　five　expert-defined　comprehensive　quality　indicators　to　assess　answer　quality,　and　(2)　using　existing　standard　answers,　provided　by　experts　for　various　question　categories,　as　external　references　to　automatically　review　the　candidate　answers.　Finally,　by　integrating　the　semantic　similarity　and　psychological　indicators,　we　make　the　final　answer　selection.　Experimental　results　have　shown　that　our　model　significantly　enhances　answer　recommendations　in　the　mental　health　domain.　　©　2024　IEEE.