Design of Improved Artificial Intelligence Generative Dialogue Algorithm and Dialogue System Model Based on Knowledge Graph

Dialogue systems are an important research direction in artificial intelligence, with broad application prospects and market value. In order to improve system efficiency and user satisfaction, an open domain generative dialogue system integrating knowledge graphs has been developed, which facilitates the utilization of rich background knowledge during dialogue generation, thereby generating more coherent and meaningful dialogue content. At the same time, based on the sequence to sequence model, a bidirectional gated loop unit is introduced to better capture contextual information and improve the model’s understanding and generation ability. These results confirmed that the average values of the improved model in the training and validation sets were 98.66% and 87.34%, respectively, with loss values of 0.01 and 0.10. Compared to the baseline model, this improved model improved Hits@1 and Hits@3 by 0.09% and 0.25%, respectively. This improved model had the minimum perplexity of 17.62. The security and diversity of this improved system were 0.80 and 0.82, respectively, taking into account the balance of these two types of performance. Its correlation and fluency were 1.44 and 1.56, respectively. This indicates that this improved model is beneficial for improving the efficiency of generating dialogue and has certain effectiveness, better meeting users’ needs and improve user satisfaction. This system can provide users with a better conversation experience and provide technological and innovative features for artificial intelligence dialogue assistants.