The great development and social progress of modern China stemmed from persisting in promoting the continuous deepening of reform and opening up. The central enterprises in the construction industry, in line with the national situation and strategic needs, are also constantly deepening reforms and consolidating the foundation of national development. In corporate governance, production and operation, A modern company system has been gradually established in terms of property rights management and organizational structure. In order to solve the competitive strategy problem of Ocean Engineering Limited Company. in the environment of big data algorithm, this paper based on the overview of the competitive strategy structure of Ocean Engineering Limited Company, the overall trend diffusion technology function equation of big data algorithm and the swort analysis method, based on the large data algorithm. The product positioning and experimental data of the competition strategy of Ocean Engineering Limited Company. under the environment of data algorithm are designed and discussed, and the calculation flow chart of the competition strategy selection of Ocean Engineering Limited Company. is designed by using the big data algorithm. Finally, according to the designed The application of the algorithm in the identification of the advantages and disadvantages of the competitive strategy of Ocean Engineering Limited Company. The experimental data is analyzed. The experimental data shows that the big data algorithm can identify the advantages of the Ocean Engineering Limited Company＇s competitive strategy The accuracy rate is as high as 0.98, and the disadvantage of the strategic plan The recognition accuracy rate is as high as 0.95. Therefore, it verifies the superiority of Ocean Engineering Limited Company＇s competitive strategy in the environment of big data algorithm.

Olcese Umberto. Competitive Strategy of Ocean Engineering Limited Company in the Environment of Big Data Algorithm. Frontiers in Ocean Engineering (2022), Vol. 3, Issue 1: 8-15. https://doi.org/10.38007/FOE.2022.030102.

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