Kernel　learning　based　on　structure　risk　minimum　can　be　employed　to　build　a　soft　measuring　model　for　analyzing　small　samples.　However,　it　is　difficult　to　select　learning　parameters,　such　as　kernel　parameter　(KP)　and　regularization　parameter　(RP).　In　this　paper,　a　soft　measuring　method　is　investigated　to　select　learning　parameters,　which　is　based　on　adaptive　multi-layer　selective　ensemble　(AMLSEN)　and　least-square　support　vector　machine　(LSSVM).　First,　candidate　kernels　and　RPs　with　K　and　R　numbers　are　preset　based　on　prior　knowledge,　and　candidate　sub-sub-models　with　K*R　numbers　are　constructed　through　utilizing　LSSVM.　Second,　the　candidate　sub-sub-models　with　same　KPs　and　different　RPs　are　selectively　fused　by　using　the　branch　and　bound　SEN　(BBSEN)　to　obtain　K　SEN-sub-models.　Third,　these　SEN-sub-models　are　selectively　combined　through　using　BBSEN　again　to　obtain　SEN　models　with　different　ensemble　sizes,　and　then　a　new　metric　index　is　defined　to　determine　the　final　AMLSEN-LSSVM-based　soft　measuring　model.　Finally,　the　learning　parameters　and　ensemble　sizes　of　different　SEN　layers　are　obtained　adaptively.　Simulation　results　based　on　the　UCI　benchmark　and　practical　DXN　datasets　are　conducted　to　validate　the　effectiveness　of　the　proposed　approach.