With the rapid development of China’s economy, the power network specifications are expanding and the network structure is becoming more and more complex. Power grid dispatching is the key to ensure the safe and stable operation of power grid. Power grid dispatch log is an important data source to reflect the operation of power grid and an important means to monitor the daily operation of power grid. Network dispatching log classification is an important application of log text analysis and mining. At present, there are many methods for network dispatching log classification, including naive bayesian method, support vector machine, neural network model and so on. However, no matter what classification method is used, scheduling log text needs to be preprocessed and converted into vector form before model training and classification. At present, the research of word vector mainly focuses on the Internet, while the feature extraction of power grid dispatch log from word vector generation is less. In this paper, a method of extracting log word vectors from power grid dispatching based on bidirectional LSTM combined dictionary is proposed. Firstly, the original log is preprocessed according to the lexicon, and word segmentation is performed on the original log by means of bidirectional LSTM combined with dictionary to obtain word segmentation results. Then, every word is transformed into a word vector through the skip-gram model. Finally, the generated word vector is used to classify the power grid dispatch logs.