Retrieval of ancient characters is a part of current digitization work of ancient books. Ancient Chinese books often exhibit inconsistent printing glyphs and a wide variety of font types， and using visual features for Chinese character retrieval is an effective solution. Therefore， a Chinese Character Feature Extraction and Encoding Network （CFEENet） was proposed. Firstly， a Convolutional Neural Network （CNN） was used to extract the visual features of Chinese character images in ancient books. Secondly， a trainable generalized vector aggregation layer， namely NetVLAD， was employed to aggregate and encode the visual features. Finally， the cosine similarity was used to calculate the code similarity to realize Chinese character retrieval in ancient books. Besides， a visual analysis of CFEENet encodes was carried out using t-distributed Stochastic Neighbor Embedding （t-SNE） after dimension reduction， and it was found that the clusters formed by CFEENet encoding had high density， small overlap between clusters， and high encoding resolution. CFEENet was tested on multiple ancient book datasets. Experimental results show that CFEENet outperforms comparison methods such as Ancient Chinese Character Image Network （ACCINet） in terms of mean Average Precision （mAP） and F1 score in most scenarios， while achieves a good balance between retrieval quality and efficiency， verifying the applicability and effectiveness of CFEENet in tasks of retrieving Chinese character in ancient books.