With the wide application of multi-task learning in non-contact WiFi perception, how to simultaneously improve the accuracy of joint activity recognition and indoor positioning tasks and maintain a balance among tasks has become a key challenge. To this end, this paper proposes an improved MMoE method to achieve joint activity recognition and indoor localization tasks. This method designs a unified and shared feature extraction layer to enhance the expressive ability of the input features. By integrating XceptionTime and ResNet, a variety of experts are constructed. The former is suitable for extracting high-frequency dynamic features to improve the accuracy of activity recognition, while the latter is suitable for modeling low-frequency static features to enhance localization accuracy. It also introduces a dual-gate mechanism and regularization constraints, effectively balancing the differences between the two tasks while enhancing the overall performance. The experimental results show that the proposed method outperforms the existing representative models in both activity recognition and indoor localization tasks, demonstrating higher accuracy and stability.