Coastal geomorphology is a key component of nearshore representation on nautical charts. However, its cartographic generalization remains constrained by incomplete knowledge-system modeling, limited task-oriented guidance, and insufficient quantitative descriptions of generalization quality. To address these limitations, this study proposes a knowledge-graph-based approach for cartographic generalization of coastal geomorphology in nautical charts. A three-layer graph framework, consisting of a concept layer, an entity layer, and a rule layer, was constructed around shorelines, coastline categories, and generalization rules. Textual knowledge extraction and spatial-feature extraction were then conducted using cartographic specifications, historical chart samples, and expert-experience documents to support the unified organization and population of multi-source knowledge. A reasoning mechanism integrating spatial constraints and semantic associations was further developed to generate quantitative reference indicators and evaluate shoreline generalization quality. The results show that the proposed knowledge graph can organize domain knowledge in a structured and traceable form, identify rule-compliance differences among different shoreline generalization results, and provide descriptive reference benchmarks for quality evaluation. The method offers a knowledge-driven basis for improving the objectivity and explainability of coastal geomorphology generalization in nautical-chart production.

To address the challenges posed by significant variations in bend morphology and spatial relationships within complex shoreline segments—where single generalization operators and uniform parameters struggle to balance node compression with morphological preservation—this paper proposes an adaptive shoreline generalization method based on bend sequence attention learning. Treating bend units as local decision objects, the method integrates geometric morphology, proximity distance, and shoreline type features. It employs bidirectional long short-term memory (BiLSTM) and self-attention mechanisms to learn contextual relationships among bends within each shoreline and predict local generalization operators and their parameters. Proximity links between shorelines support spatial consistency checks during constraint-based correction. After operator execution, candidate geometries that violate cartographic constraints undergo parameter rollback or operator reselection; unresolved bends are restored to their original geometry before output. Under the evaluated datasets, scoring procedure, and compression settings, the final generalized shorelines achieved a vertex-reduction ratio of 86.43% and a bend-retention ratio of 94.84%. These results support the feasibility of bend-level adaptive operator selection at the evaluated compression–preservation operating point. Performance across other compression settings and chart series remains to be established.