In view of the inadequacy of interactivity and ecological aesthetics in park landscape, the existing methods are often difficult to achieve accurate terrain analysis and design, resulting in unsatisfactory landscape effects. In order to make up for this shortcoming, the research innovatively integrates sparrow search algorithm (SSA) and self-organizing mapping algorithm (SOM) to conduct in-depth analysis of park landscape topography. SSA, with its powerful global search ability, can quickly lock the optimal solution region. SOM uses unsupervised learning to effectively mine the underlying rules in terrain data. The combination of the two not only improves the accuracy and efficiency of terrain analysis, but also lays a solid foundation for the construction of park landscape environment interaction design model. The root-mean-square error of the algorithm is only 0.01 m², the accuracy is as high as 98.1%, and the F1-value is also as high as 97.8%. Compared with previous studies, the algorithm has better fitting performance. In addition, the model is applied to the actual park landscape design, and an interactive park landscape optimization design scheme is proposed based on the design concept of forest walk and ecological protection area. After evaluation, the program scored 9.1 points for interactivity and 9.2 points for sustainability, fully proving that it meets the interactive needs of tourists while also taking into account ecological protection and sustainability. Compared with previous studies, the innovation of this study lies in the successful integration of SSA and SOM algorithms, which significantly improves the accuracy of terrain analysis, and verifies the practicability and effectiveness of this method through practical application. Overall, the method proposed in the study can analyze topography more accurately than traditional methods and design park landscapes that are both interactive and aesthetically pleasing.

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