Radar emitter signal recognition is an important means to defeat the enemy on the actual battlefield. To solve the problems of incomplete characteristic parameters and low timeliness of an artificial extraction's radar emitter signal, based on the unique role of the ambiguity function in characterizing the internal structure of the signal, this study proposes a recognition method for convolutional bidirectional long- and short-term memory network combined with the transformation of the main ridge coordinate of the ambiguity function. First, to amplify the difference between different signals, the main ridge section was mathematically converted into a geometric image in the polar coordinate domain, which was used as the input of a neural network. Second, a convolutional neural network was designed to excavate the feature information of a two-dimensional time-frequency map. Finally, a bidirectional long- and short-term memory network was built to classify and recognize the extracted features. Simulation results show that the proposed method maintains 100% accuracy even when the signal-to-noise ratio is above 0 dB, the recognition rate reaches above 93.58% even at -6 dB, and the signal classification time is effectively shortened. Furthermore, the proposed method extracts the hidden abstract features of the signal and produces good timeliness and antinoise performances.