Analysis of Big Data Embedded Mapping Model Oriented to Prediction of Subordinate Relationship

Abstract

With the rapid development of information technology, research on big data information has gone deep into all walks of life, and massive amounts of data spew out anytime and anywhere. This paper proposes a domain-subordinate relationship acquisition that combines multiple strategies and word representations. From semi-structured text and unstructured text, the candidate entity upper-lower relationship entity pairs are extracted, and then the obtained candidate upper-lower relationship entity pairs are verified by the support vector machine method to obtain high-quality candidate entity pairs. In the training phase, an exploratory method is designed to search for the optimal solution, and a greedy mechanism is introduced to evaluate the effectiveness of the reinforcement learning agent, so that the virtual node mapping scheme can continuously move towards higher system returns. Evolution has finally achieved the optimal virtual network mapping decision, so that large-scale tasks can be deployed to task processing nodes in the appropriate underlying network to achieve efficient task execution in a big data environment. The experimental results show that the Tard method can effectively avoid model overfitting and improve the task recognition processing ability in the actual application process under the premise of meeting large-scale task requests.