Sound event detection models based on deep learning typically require a substantial mount of labeled data to train from scratch. Access to task-specific data is costly due to restrictions such as data access rights， usage licenses， and the scarcity of rare individual samples. In order to address the challenge of few shot in sound event detection， this paper proposes a model-agnostic and gradient-balanced meta learning algorithm based on model-agnostic meta learning （MAML）. This algorithm trains the model with a large quantities of N-way K-shot tasks， enabling it to acquire the ability of rapid learning， accurately discriminating the unheard sound event in the N-way K-shot target task with minimal gradient updates. In the outer loop stage， the multi-gradient descent algorithm is used to estimate the dynamic loss balance factor， encouraging the model to focus on few-shot training tasks that are more difficult to train， thereby enhancing the shared representation of the model. Furthermore， this paper incorporates data augmentation and label smoothing to mitigate the risk of overfitting caused by the scarcity of training samples. Experimental results demonstrate that the algorithm achieves 73.56%， 82.86% and 57.48% accuracies in the 5-way 1-shot setting on the ESC50， NSynth and DCASE2020 datasets， respectively， showing about 10% relative accuracy improvement compared to the previous MAML algorithm.