Concerning the complicated process, interdisciplinarity, and poor real-time performance of enterprise named entity recognition, a method based on concurrent subspace optimization is proposed. First, a target-constrained equation of the system is established to complete system-level optimization; secondly, a two-level model of text detection and recognition is constructed, and the model is selected, considering the advantages and disadvantages of different existing models, to optimize the discipline in parallel; then, the connection of the two-level model is constructed with the image threshold, grayscale and Hoff transform; finally, simulation experiments verify that the recognition accuracy of this method is 9% higher than that of other two-level text detection and recognition models, and the speed increases by about 20%.