Aiming at the problem of poor recognition of nested entities and long entities in the Chinese automotive domain entity extraction task， an Entity Category Enhanced nested Named Entity Recognition （ECE-NER） model was proposed. Firstly， the model’s perception of domain entity boundaries was improved based on feature fusion encoding. Then， the tail word recognition module was used to obtain the entity tail word set by multi-layer perceptron. Finally， the forward boundary recognition module was used to obtain entity category-enhanced entity representation of candidate tail words， based on the sememe-constructed entity category features and self-attention mechanism. By fusing domain entity category features， a biaffine encoder was used to calculate the entity span probabilities of the specific tail words in order to determine the named entities. The experimental evaluation was carried out on the failure dataset of the automobile production line， the failure extraction and evaluation dataset of the automobile industry CCL2022， and the Chinese medical text dataset CHIP2020. The experimental results on the first two datasets show that ECE-NER model increases F1 value by 4.1， 1.8， 1.6 percentage points and 9.0， 5.4， 7.3 percentage points respectively compared with the baseline models including the sequence labeling model （BERT+BiLSTM+CRF） and the span-based entity extraction models （PURE（Princeton University Relation Extraction）， SpERT（Span-based Entity and Relation Transformer））. Especially， ECE-NER model increases F1 value of nested entity recognition by 13.3， 8.3 and 21.7， 9.3 percentage points in the first and third datasets compared to PURE and SpERT models. The experimental results verify the effectiveness of the proposed model on the recognition of nested entities.