Abstract
The current data mining algorithm has the problem of imperfect data mining function, which leads to the algorithm taking too long time. This paper designs a data mining algorithm based on BP neural network. Analyze the basic structure of the data mining algorithm, obtain the data characteristics of the multi-objective decision-making, adjust the convergence speed with the distributed computing technology to keep the inertia factor state unchanged, construct the local minimal discrete model, measure the interest of the model, calculate the optimal output value of the network using the BP (Back Propagation) neural network model, and complete the improved design of the data mining function. Experimental results: The average computational time consumption of the designed data mining algorithm is 559.827 seconds, which saves 145.975 seconds and 174.237 seconds respectively than other traditional algorithms. It is proved that the data mining algorithm based on BP neural network reduces the computational time consumption, improves the performance of data mining, and has high application value.
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