Hybrid classification neural network
Basically, you need to create a whole system which contains multiple ML algorithms. We can go feature-wise and see how the system could work.


Description: This will consist of text which could be easily vectorized using Doc2Vec. This vector will act as a feature for the final model.
Product images: You will need to create a model which make some sort of classification. Once, the model is trained to remove the last layer ( mostly the Softmax layer ). This will fetch us the encoded representation of the image. You may use an AutoEncoder for this.
Manufacturer and other properties: I think you may need to omit this feature as they will be product-specific and create complexities while training.


Once, we have all the above models trained, we can feed the features ( generated from these models ) to a neural network, which will produce the final result.