  With the growing impact of ESG on businesses, research related to renewable
energy is receiving great attention. Solar cells are one of them, and
accordingly, it can be said that the research value of solar cell patent
analysis is very high. Patent documents have high research value. Being able to
accurately analyze and classify patent documents can reveal several important
technical relationships. It can also describe the business trends in that
technology. And when it comes to investment, new industrial solutions will also
be inspired and proposed to make important decisions. Therefore, we must
carefully analyze patent documents and utilize the value of patents. To solve
the solar cell patent classification problem, we propose a keyword extraction
method and a deep neural network-based solar cell patent classification method.
First, solar cell patents are analyzed for pretreatment. It then uses the
KeyBERT algorithm to extract keywords and key phrases from the patent abstract
to construct a lexical dictionary. We then build a solar cell patent
classification model according to the deep neural network. Finally, we use a
deep neural network-based solar cell patent classification model to classify
power patents, and the training accuracy is greater than 95%. Also, the
validation accuracy is about 87.5%. It can be seen that the deep neural network
method can not only realize the classification of complex and difficult solar
cell patents, but also have a good classification effect.
