Research on cognitive computing model based on machine learning algorithm in artificial intelligence environment

In the artificial intelligence environment, constructing cognitive computational models using machine learning algorithms is the main direction of computer development. By outlining the three cognitive levels of machine learning, the feature space composition of the cognitive computational model is exposed based on the data acquired by the human brain monitoring equipment. The Gaussian decision tree algorithm is used to construct the cognitive computation model, and the anthropomorphic effects of machine cognitive computation are explored in two directions: auditory features and visual features. In terms of auditory features, the model in this paper maintains 95.03% ± 2.49% feature recognition rate. In contrast, in terms of visual features, the algorithm proposed in this paper maintains a high tracking success rate of 88.83%. Based on the auditory and visual feature analysis results, the cognitive computing model based on the Gaussian decision tree algorithm has been confirmed to perform excellently. © 2024 Xiaolei Zhang et al., published by Sciendo.