The　Municipal　solid　wastes　incineration　(MSWI)　process　with　complex　mechanism　and　strong　nonlinearity　is　an　important　part　of　resource　cycle.　It　is　a　challenge　to　design　its　multivariable　controlled　object　model　and　control　strategy.　To　solve　this　problem,　a　multi-input　multi-output　(MIMO)　data-driven　model　and　a　multi-loop　PID　controller　are　proposed　in　this　paper.　Firstly,　a　feature　selection　method　based　on　Pearson　correlation　coefficient　(PCC)　and　expert　knowledge　is　used　to　analyze　the　relationship　between　the　manipulated　variable　and　the　controlled　variable　of　MSWI　process.　Secondly,　a　MIMO　Takagi-Sugeno　fuzzy　neural　network　(TSFNN)　based　on　multi-task　learning　(MTL)　is　designed　to　construct　the　multivariable　controlled　object　model.　Thirdly,　a　multi-loop　PID　controller　based　on　quasi-diagonal　recurrent　neural　network　(QDRNN)　is　constructed,　which　has　self-feedback　channel　and　interconnection　channel,　and　can　adjust　the　control　parameters　automatically.　Next,　the　stability　of　control　strategy　is　proved　by　Lyapunov　second　method.　Finally,　the　modeling　effect　and　control　performance　are　confirmed　on　the　simulation　experiments　based　on　the　real　MSWI　process　data.