Visual　affordance　recognition　is　an　important　research　topic　in　robotics,　human-computer　interaction,　and　other　computer　vision　tasks.　In　recent　years,　deep　learning-based　affordance　recognition　methods　have　achieved　remarkable　performance.　However,　there　is　no　unified　and　intensive　survey　of　these　methods　up　to　now.　Therefore,　this　article　reviews　and　investigates　existing　deep　learning-based　affordance　recognition　methods　from　a　comprehensive　perspective,　hoping　to　pursue　greater　acceleration　in　this　research　domain.　Specifically,　this　article　first　classifies　affordance　recognition　into　five　tasks,　delves　into　the　methodologies　of　each　task,　and　explores　their　rationales　and　essential　relations.　Second,　several　representative　affordance　recognition　datasets　are　investigated　carefully.　Third,　based　on　these　datasets,　this　article　provides　a　comprehensive　performance　comparison　and　analysis　of　the　current　affordance　recognition　methods,　reporting　the　results　of　different　methods　on　the　same　datasets　and　the　results　of　each　method　on　different　datasets.　Finally,　this　article　summarizes　the　progress　of　affordance　recognition,　outlines　the　existing　difficulties　and　provides　corresponding　solutions,　and　discusses　its　future　application　trends.