Recognizing　key　entities　on　texts　of　water　environment　accurately　and　rapidly　can　not　only　extract　important　information　of　water　environment,　but　also　improve　the　water　quality.　In　recent　years,　Chinese　named　entity　recognition　becomes　a　research　focus　and　many　methods　based　on　neural　networks　have　been　proven　effective　on　entity　recognition.　This　work　proposes　an　improved　hybrid　prediction　model　named　LBPSC　for　Chinese　named　entity　recognition　for　the　water　environment　data,　which　combines　Lattice　structure,　Bi-directional　long　short-term　memory　(BiLSTM),　Positional　feature　encoding,　Sentence　self-attention　and　conditional　random　field　(CRF).　LBPSC　employs　a　three-phase　end-to-end　methodology　for　Chinese　named　entity　recognition.　It　first　adopts　a　BiLSTM　with　lattice　structure　to　extract　both　character　and　word　features　from　two　directions,　thereby　avoiding　word　segmentation　errors.　It　then　innovatively　combines　a　sentence　self-attention　mechanism　with　positional　feature　encoding　to　better　handle　sentences　and　add　the　position　information　to　the　trained　features　after　BiLSTM.　Then,　a　CRF　layer　is　adopted　to　decode　features　and　finally　output　the　predicted　tag　of　the　data.　Experimental　results　with　real-life　dataset　demonstrate　that　LBPSC　outperforms　other　deep　learning　algorithms　in　terms　of　prediction　accuracy.　©　2022　IEEE.