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As legal case judgment documents begin to be published on the Internet, legal data is increasing. At the same time, with
the continuous improvement of computer computing power, the continuous development of data mining, machine
learning, deep learning and other technologies, the era of legal big data must Will come. This article has carried out in-depth
excavation of judgment documents of legal cases, in order to provide an objective and fair reference for the
sentencing of legal cases, and proposed a legal case decision-making system based on neural network. This article first
extracts information in accordance with certain regular rules from the judgment documents of legal cases, and uses the
random forest model to predict the sentences in the judgment documents. According to the specific details of the case,
determine the existence of various sentencing circumstances in the case, and use it as a characteristic quantity for
classification and decision-making. In order to improve the decision-making performance of the network, in view of the
local minima and false saturation existing in gradient learning, a learning algorithm based on neural network is proposed,
which further improves the decision-making ability of the network. Simulation experiments show that the decision-making
network can give an objective and fair sentencing result.
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