The invention belongs to the field of artificial intelligence education, and particularly relates to a multi-dimensional visual multi-source heterogeneous data multi-layer DRNN deep fusion method. Themethod comprises the following steps: 1, establishing a DRNN neural network; inputting the multi-source heterogeneous data into a DRNN neural network to form a DRNN training template; 3, establishingforward output of a single-layer neuron individual of the DRNN network; wherein the single-layer network is used as an activated fusion function; 4, establishing weight reconstruction and correctionof the DRNN network; wherein the correction exists as a fusion function; and step 5, obtaining feature output through data feature fusion in a data fusion layer of the DRNN. The invention provides a fusion architecture, which can perform feature level fusion for different multi-source heterogeneous data.
Multi-dimensional visual multi-source heterogeneous data multi-layer DRNN deep fusion method