A signal identification device for classifying an input signal data, the input signal data being a radio wave signal acquired by a radar or an optical image, comprising a learning unit including a known signal learning unit configured to generate a learned model based on signal data and teacher data; an inference model using a neural network, which generates, based on the input signal data, a first latent variable (z) in which a distribution for each class in a latent space is defined according to the class of classification, and a second latent variable (m) in which a distribution for each large classification in the latent space is defined according to the large classification of a broader concept of the class, the first latent variables and the second latent variables being generated from the latent space including variables of input data and variables based on teacher data, and an inference unit configured to output, using the inference model, an identification result of the input signal data, wherein the inference unit further includes a signal identification unit configured to determine an equal probability curve representing n% section in the distribution for each class as a boundary for recognizing that the input signal data belongs to the class, wherein the distribution is a Gaussian distribution defined in the latent space for each learned class, and wherein the signal identification unit determines that the input signal data belong to a class if its corresponding latent variable is inside the equal probability curve of that class, wherein the inference model is learned using both signal data and teacher data, the teacher data including information of the class to which the signal data belongs and information of the broader concept of the class. a learning unit including a known signal learning unit configured to generate a learned model based on signal data and teacher data; an inference model using a neural network, which generates, based on the input signal data, a first latent variable (z) in which a distribution for each class in a latent space is defined according to the class of classification, and a second latent variable (m) in which a distribution for each large classification in the latent space is defined according to the large classification of a broader concept of the class, the first latent variables and the second latent variables being generated from the latent space including variables of input data and variables based on teacher data, and an inference unit configured to output, using the inference model, an identification result of the input signal data, wherein the inference unit further includes a signal identification unit configured to determine an equal probability curve representing n% section in the distribution for each class as a boundary for recognizing that the input signal data belongs to the class, wherein the distribution is a Gaussian distribution defined in the latent space for each learned class, and wherein the signal identification unit determines that the input signal data belong to a class if its corresponding latent variable is inside the equal probability curve of that class, wherein the inference model is learned using both signal data and teacher data, the teacher data including information of the class to which the signal data belongs and information of the broader concept of the class.