It is importantly used in the fields such as creation of large-scale professional talent pools to extract scholar fine-grained information such as scholar’s research directions, education experience from scholar homepages. To address the problem that the existing scholar fine-grained information extraction methods cannot use contextual semantic associations effectively, a scholar fine-grained information extraction method incorporating local semantic features was proposed to extract fine-grained information from scholar homepages by using semantic associations in the local text. Firstly, general semantic representation was learned by the full-word mask Chinese pre-trained model RoBERTa-wwm-ext. Subsequently, the representation vector of the target sentence, as well as its locally adjacent text representation vector from the general semantic embeddings, were jointly fed into a CNN (Convolutional Neural Network) to accomplish local semantic fusion, thereby obtaining a higher-dimensional representation vector for the target sentence. Finally, the representation vector of the target sentence was mapped from the high-dimensional space to the low-dimensional labeling space to extract the fine-grained information from the scholar homepage. Experimental results show that the micro-average F1 score of the scholar fine-grained information extraction method fusing local semantic features reaches 93.43%, which is higher than that of RoBERTa-wwm-ext-TextCNN method without fusing local semantic by 8.60 percentage points, which verifies the effectiveness of the proposed method on the scholar fine-grained information extraction task.