In the existing community planning and implementation, the design concept often does not take into account the needs of children, but only blindly pursues standardization and modernization. This would make the entire community structure unfriendly to children. Factors such as insufficient public play areas, messy traffic network design, and lack of recreational facilities suitable for children would limit children’s fun in the community’s public spaces. Children’s happy growth would be greatly hindered. In order to solve the above problems, this paper has put forward the child-friendly concept and has applied it to community planning, so as to carry out scientific research on the original participatory community planning model based on the support vector machine technology under the machine learning algorithm. And the improved gray wolf optimization algorithm is combined with it for the shortcomings of the technology’s algorithm accuracy and other performance, which is not good enough. The final optimization algorithm has played an extremely important role in testing the impact of the participatory community planning model on publicity in the renewal of public spaces. The experimental results have shown that the highest accuracy of the improved gray wolf optimization support vector machine technology can reach 97.88% and the highest Kappa coefficient can reach 96.83%, which greatly improves the feasibility of the research on the impact of public space publicity.