The invention discloses a text classification method and device based on a heterogeneous neural network, and the method comprises the steps: S1, constructing an N-layer text classification multi-way tree corresponding to a
tree structure in combination with the
tree structure of an actual classification
system in a training
data set, and according to the structure of the N-layer text classification multi-way tree, respectively writing training data in the training
data set into classification files corresponding to each level, performing word segmentation on Chinese texts of each classification file, performing
feature selection, and storing the selected features into corresponding feature files; S2, constructing a text heterogeneous neural network corresponding to the
tree structure; and S3, setting heterogeneous neural network
algorithm parameters, carrying out parameter adjustment, iteratively generating and storing each classification and sub-classification model of training data, using a
verification data set for carrying out accuracy judgment. The categories and the
hierarchical relation between the categories are also added into the heterogeneous neural network, and the problems of vector representation and
link data sparsity are learned in a category display manner.