Chemical　oxygen　demand　value　is　a　key　indicator　for　water　quality　detection　and　an　important　link　in　water　environmental　protection.　Near　infrared　spectroscopy　is　a　fast,　non-destructive,　and　green　identification　method　applied　to　chemical　oxygen　demand　detection.　Near　infrared　spectroscopy　has　the　characteristics　of　high　data　dimensions,　severe　band　stacking,　and　difficult　feature　extraction.　In　this　paper,　an　improved　one-dimensional　convolutional　neural　network　is　proposed　to　analyze　the　near　infrared　spectroscopy　of　water　samples　to　predict　their　chemical　oxygen　demand　values.　Firstly,　one-dimensional　convolution　is　used　to　extract　deep　spectral　features.　Secondly,　SoftPool　is　used　to　reduce　the　dimension　of　spectral　features　to　improve　the　problem　of　losing　some　features　in　traditional　pooling　methods.　Finally,　the　prediction　results　are　output　through　full　connectivity　layer　integration　features.　The　proposed　method　is　validated　on　a　near　infrared　spectral　dataset　of　chemical　oxygen　demand.　The　experimental　results　show　that　this　method　can　accurately　and　quickly　analyze　the　chemical　oxygen　demand　value　in　water.　©　2023　SPIE.