The application discloses a
natural source search method based on an SVM improved
convolutional neural network, and comprises the following steps: S1, collecting different formats of languages, and importing the languages into a conversion program in a
plaintext mode; S2, importing mark information in a newly generated language
database into a language generation program, analyzing
feature data imported into the language generation program by using a pre-constructed deep neural
network model, and automatically generating a general
functional description language; S3, randomly selecting two literal languages from the general
functional description language, learning a common occurrence property between words of each source language, converting the property into a low-dimensional real value
distributed representation, and forming a resource dictionary vector; and S4, identifying the resource dictionary vector based on the SVM improved
convolutional neural network. The
natural source search method based on the SVM improved
convolutional neural network increases
spatial relation judgment capability and improves search efficiency.