Microorganisms have been confirmed to be essential for the fundamental function of various ecosystems. The interactions among microorganisms affect the human health and environmental ecosystem. A large number of microbial interactions with experimental confidence have been reported in biomedical literature. Extracting and collating these interactions with experimental confidence into a database will create a valuable data resource. Named Entity Recognition (NER) is the premise and key to interaction extraction from literatures. Especially, bacterial named entity recognition is still a challenging task due to the specialty of bacterial names. In this paper, we propose a bacterial named entity recognition system based on a hybrid deep learning framework (HDL-CRF), which integrates two deep learning models: the bidirectional long short-term memory network and the convolutional neural network, as well as the conditional random field approach, for automatically extracting the features. Finally, we prove that this model outperforms previous methods in performance.