In　recent　years,　there　has　been　a　great　deal　of　interest　in　the　task　of　machine　reading　comprehension.　It　enables　computers　to　learn　and　answer　questions　based　on　text　input.　One　of　the　long-standing　challenges　in　the　field　of　artificial　intelligence　is　how　to　make　machines　understand　natural　language.　In　recent　years,　machine　reading　comprehension　has　advanced　rapidly　as　a　result　of　the　large-scale　release　of　high-quality　data　sets　and　the　application　of　deep　learning　technology.　The　use　of　an　end-to-end　model　structure　based　on　neural　networks,　a　pre-trained　language　model,　and　reasoning　technology　has　greatly　improved　their　performance　on　large-scale　evaluation　data　sets.　However,　there　is　still　a　big　gap　in　real　language　understanding.　This　paper　summarizes　the　research　status　and　development　trend　of　machine　reading　comprehension　tasks,　including　division　of　tasks,　analysis　of　machine　reading　comprehension　model　and　related　technologies,　particularly　machine　reading　comprehension　technology　based　on　knowledge　reasoning,　and　finally　dis-cusses　the　development　trend　in　this　field.　©　2022,　Editorial　Department　of　CAAI　Transactions　on　Intelligent　Systems.　All　rights　reserved.