The groundwork of an intelligent recorder is to map the address information of Intelligent Electronic Device (IED) data output ports within the Substation Configuration Description (SCD) file to different recorder information groups. At present, the mainstream mapping method is manual configuration based on the text description of the output port address. In a large-scale and high-voltage substation, output port address text descriptions are numerous and diverse. The manual operation often takes a huge amount of time. Also the labor cost is high. In order to improve the situation, this paper proposes an automatic information configuration method for an intelligent recorder based on a Text Convolutional Neural Network (TextCNN), which deeply learns the semantics of tests at the character level. First, to realize the distributive expression of text character vectors, the word2vec is introduced to thicken and reduce the dimension of the sparse and high-dimensional sample vector matrix. Secondly, this paper establishes a TextCNN model for text semantic analysis. Based on its ability to obtain multi-level abstract features of samples, the text semantic mining is therefore performed and its classification is realized. The mapping of the IED output port address information is completed based on the text classification result. The case study shows that the automatic intelligent recorder information configuration based on the TextCNN model has the characteristics of short classification time and high accuracy. This improves the accuracy of the automatic configuration for the intelligent recorder. This work is supported by the Science and Technology Project of China Southern Power Grid Co., Ltd. (No. 000000KK 52180019).