With the arrival of the big data era, massive data storage, massive information push, and massive complex products and services are flooding the whole society. As an important and effective method, machine learning model has been widely used in various fields. In order to apply data mining technology to practice well, this paper makes an in-depth study on machine learning model and data mining optimization methods. This paper mainly uses the methods of experiment and comparison, and puts forward the advantages of various improved algorithms through the detection of fragments, orchids and beverages. The experimental results show that the C index obtained by ESPSO-FCM on fragments is 0.523, which is larger than the other two algorithms. ESPSO-FCM algorithm is an improved clustering algorithm with higher convergence accuracy, stronger partition ability and better clustering quality.

Yongfeng Shi. Optimization Algorithm of Big Data Mining Based on Machine Learning Model. Machine Learning Theory and Practice (2023), Vol. 4, Issue 1: 61-69. https://doi.org/10.38007/ML.2023.040108.

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