Rely on powerful computing resources, a large number of internet of things (IoT) sensors are placed in various locations to sense the environment around where we live and improve the service. The proliferation of IoT end devices has led to the misuse of spectrum resources, making spectrum regulation an important task. Automatic modulation classification (AMC) is a task in spectrum monitoring, which senses the electromagnetic space and is carried out under non-cooperative communication. However, DL-based methods are data-driven and require large amounts of training data. In fact, under some non-cooperative communication scenarios, it is challenging to collect the wireless signal data directly. How can the DL-based algorithm complete the inference task under zero-sample conditions? In this paper, a signal zero-shot learning network (SigZSLNet) is proposed for AMC under the zero-sample situations firstly. Specifically, for the complexity of the original signal data, SigZSLNet generates the convolutional layer output feature vector instead of directly generating the original data of the signal. The semantic descriptions and the corresponding semantic vectors are designed to generate the feature vectors of the modulated signals. The generated feature vectors act as the training data of zero-sample classes, and the recognition accuracy of AMC is greatly improved in zero-sample cases as a consequence. The experimental results demonstrate the effectiveness of the proposed SigZSLNet. Simultaneously, we show the generated feature vectors and the intermediate layer output of the model.