Quantile　regression　has　been　attractive　due　to　its　interpretability　and　robustness,　which　is　a　useful　tool　in　regression　analysis.　Moreover,　the　relationship　between　response　and　some　covariates　is　complex　nonlinearity　in　real　data,　and　there　probably　exists　high-order　interaction　effects　between　covariates.　Furthermore,　the　irrelevant　covariates　included　in　the　model　lead　to　unsatisfactory　prediction　results.　Inspired　by　this,　we　propose　a　novel　estimation　and　variable　selection　method　of　the　semiparametric　quantile　regression.　The　unknown　function　is　estimated　by　the　kernel　machine　technique,　and　the　LASSO　penalty　is　applied　to　achieve　variable　selection　in　the　nonlinear　part.　Importantly,　we　introduce　slack　variables　to　solve　the　limitations　of　the　quantile　loss　function　in　optimization　problems,　and　solve　the　optimization　problem　of　nonlinear　functions　through　local　linear　approximation　technology.　The　proposed　estimator　is　easy-to-implement　via　an　efficient　cyclical　coordinate　descent　algorithm.　Both　simulations　and　real　applications　demonstrate　the　convincing　performance　of　the　proposed　estimator.