Fine-grained entity typing is a multi-class and multi-label task, which can help a wide range of downstream tasks (relationship extraction, co-reference resolution, question answering system, etc. ) to enhance productivity and optimize accuracy. It has become a research hotspot in natural language processing field. In view of the difficulty and low accuracy of the traditional fine-grained entity typing method to annotate large corpus, researchers proposed the fine-grained entity typing method based on neural network, which can not only solve the time-consuming and laborious problem of manual annotation, but also improve the accuracy of classification. However, most of the existing neural network models require the participation of distant supervision, which will introduce noise labels and other problems in the process. The noise labels processing method can effectively suppress the impact of noise labels on the classification results and further improve the classification performance. Under the same evaluation datasets, we compared the performance of various fine-grained entity typing methods according to the same evaluation metrics. It can be found that in the field of fine-grained entity typing, using BiLSTM to process the context of entity mention and extracting more important features through the attention mechanism are helpful to improve the accuracy, Macro F1 value and Micro F1 value of fine-grained entity typing method.