Being　a　commonly　used　way　for　task　decomposition　in　modular　neural　network　(MNN),　clustering　analysis　is　employed　to　decompose　the　complex　task　into　several　simple　subtasks　for　learning.　Recent　studies　mainly　focus　on　hard　clustering,　but　the　clusters　might　be　not　sufficiently　represented　when　the　cluster　boundary　is　ambig-uous,　which　may　degenerate　the　learning　performance　of　subnetworks　in　MNN.　To　solve　this　problem,　we　design　a　modular　neural　network　based　on　an　improved　soft　subspace　clustering　(IESSC-MNN)　algorithm　in　this　study.　Firstly,　we　propose　an　improved　soft　subspace　clustering　algorithm　for　task　decomposition　in　MNN,　which　di-vides　the　original　space　into　several　interactive　feature　subspaces　and　allocates　a　weight　item　to　each　subspace　to　describe　the　contribution　of　the　subtasks　at　the　same　time.　Secondly,　each　RBF　subnetwork　is　adaptively　con-structed　using　a　structure　growing　strategy,　and　all　subnetworks　learning　the　corresponding　subtask　in　parallel.　Finally,　all　subnetworks＇　outputs　are　integrated　by　weighted　summation　using　the　contribution　weight　of　sub-networks.　The　simulation　results　of　the　proposed　model　on　five　benchmark　data　and　a　practical　dataset　indicate　that　IESSC-MNN　improves　the　modeling　accuracy　and　generalization　performance　with　a　simple　structure　when　compared　with　other　MNNs.