Despite the significant advancements in computer speech emotion recognition technology, the deployment of intelligent robots in this domain continues to encounter challenges related to inefficiency and emotional ambiguity. Aiming at the shortcomings of existing algorithms in the accuracy and processing of complex emotion states, a novel emotion recognition model of intelligent robot speech based on ACO algorithm and multi-level support vector machine is proposed. This paper first identifies the efficiency and accuracy problems existing in the current emotion recognition system, and clarified the research gap, that is, how to effectively improve the recognition performance of the model under diverse emotion states. To solve this problem, genetic algorithm is used to fine-tune SVM model parameters, and multi-level SVM classification architecture is constructed to enhance the accuracy of emotion recognition. In the feature selection stage, ACO algorithm is introduced to explore the optimal combination of emotion features, aiming at improving the efficiency and robustness of emotion recognition. The experimental results showed that the average recognition rate of the multilevel SVM model optimized by genetic algorithm was as high as 81.26 %, which was 8.78 percentage points higher than that before optimization. When the ACO algorithm was combined with the optimized multi-stage SVM, the average recognition rate of German and Chinese speech emotion corpus was further improved to 85.62 %, with an improvement of 4.38 percentage points. In particular, the optimized algorithm performed significantly better than other emotion categories in recognizing complex emotions such as ''anger'' and ''sadness.'' This paper not only offers an innovative solution for the emotion recognition technology within the realm of intelligent robots, but also extends its methodology to have significant implications for related fields, thereby strongly advancing the in-depth application and development of intelligent bionic algorithms in speech emotion recognition. In addition, through this series of optimization strategies, this paper significantly improves the accuracy of emotion recognition, laying a solid foundation for intelligent robots to achieve more natural and efficient human-computer interaction.