Knowledge　graph　of　landscape　plants　provides　potential　uses　in　the　selection　of　greening　tree　species　considering　regional　adaptability,　ornamental　and　ecological　factors.　Entity　and　relationship　extraction　of　the　plant＇s　description　text　is　a　key　issue　in　the　construction　of　knowledge　graph.　Until　now,　there　has　been　no　publicly　available　annotated　data　set　for　the　plant　domain.　In　this　paper,　a　conceptual　architecture　of　landscape　plants　was　defined　and　briefly　described,　and　the　landscape　plant　corpus　was　constructed.　Existing　language　models　such　as　word2vec,　ELMo,　and　BERT　have　various　disadvantages,　e.g.,　they　can＇t　solve　the　problem　of　polysemous　words　and　have　poor　ability　of　context　fusion　and　computational　efficiency.　In　this　paper,　we　proposed　a　named　entity　recognition　model,　ALBERT-　BiGRU-　CRF,　and　a　relationship　extraction　model,　ALBERT-BiGRU-Attention,　which　were　embedded　with　ALBERT　(A　Lite　Bidirectional　Encoder　Representation　from　Transformers)　pre-training　language　model.　In　the　ALBERT-BiGRU-CRF　model,　the　ALBERT　model　was　used　to　extract　text　features,　the　Bi-GRU　model　was　used　to　learn　and　excavate　deep　semantic　features　between　sentences,　and　the　CRF　model　was　used　to　calculate　the　probability　distribution　of　the　annotation　sequence　to　determine　the　entities　contained　in　the　description　text.　The　ALBERT-BiGRU-Attention　model　was　based　on　the　results　of　the　named　entity　recognition　model.　Similarly,　the　attention　model　was　used　to　improve　the　weight　of　keywords　to　determine　the　relationship　between　entities.　The　proposed　models　have　the　following　advantages:　(1)　The　method　can　effectively　identify　and　extract　entities　and　relationships　of　landscape　plants＇　knowledge;　(2)　The　models　can　represent　the　semantic　and　sentence　characteristics　of　characters　with　a　good　accuracy.　The　validity　of　the　method　was　verified　on　the　landscape　plant　corpus　constructed　in　this　paper　and　compared　with　other　models.　Our　experimental　results　of　quantitative　evaluation　show　that:　(1)　The　F1　index　of　the　ALBERT-　BiGRU-CRF　model　was　0.9517,　indicating　that　it　had　good　performance　in　named　entity　recognition　task　and　can　effectively　identify　23　main　entity　types;　(2)　After　comparative　experiments　and　analysis　of　the　relationship　extraction　results,　the　F1　index　of　the　ALBERT-BiGRU-Attention　model　was　0.9161,　indicating　that　it　performed　well　in　the　relationship　extraction　of　landscape　plants;　(3)　By　selecting　6　representative　examples　to　further　evaluate　the　extraction　performance　of　this　method,　the　results　show　that　the　method　can　well　identify　the　knowledge　triples　of　common　single-　relation　and　multi-　relation　texts.　Therefore,　the　entity　relationship　extraction　task　based　on　ALBERT　model　can　effectively　improve　the　recognition　and　extraction　results.　It　can　be　applied　to　the　entity　relationship　extraction　task　of　plant　description　text,　providing　a　method　for　automatic　construction　of　landscape　plant　knowledge　graph.　©　2021,　Science　Press.　All　right　reserved.