Application of dynamic database based on machine learning in enterprise intellectual property management

The enterprise intellectual property data management specification system based on machine learning is a modern approach to managing and analyzing large amounts of data. Machine learning algorithms, such as neural networks, can be used to classify and query data, providing a more efficient and accurate way to manage intellectual property data. The system is built using a neural network as an encoder and decoder, which can be trained to classify and reconstruct the data. For binary classification problems, the cross-entropy is used as the loss function to measure the error between the reconstructed data and the original data. And the joint probability density of the entire database table is approximately expressed by improving the automatic encoder to handle a variety of approximate queries. In order to ensure the balance of data samples, a comprehensive connection sampling framework is adopted in the sampling process, which can select different samples with uniform distribution from the complex scenes of dynamic database for analysis. Considering the different types of data, the quantitative data are evaluated with the help of standardized data processing methods. In order to test the accuracy of the data classification model, the confusion matrix method is used to predict and classify each class of representative samples to judge the accuracy and error rate of sample data prediction. Finally, the performance of the enterprise intellectual property data management standard system is evaluated and compared with the time of manual preparation of enterprise intellectual property data management, which proves the feasibility of the system and the effectiveness of efficiency improvement.