We study the method of family activity recognition based on audio and propose a capsule neural network recognition model based on additive margin. In view of the drawbacks of the traditional capsule neural network objective function only with the output capsule mode length as the constraint

this paper adds a Transition layer to the capsule neural network structure from the perspective of geometry and uses the Transition layer to rebase the capsule unit spatial relationship to the one-dimensional. Then

using the additive margin Softmax as the objective function

the change of similar features is small

and the difference of non-similar features is used as the optimization strategy to construct the objective function based on the capsule vector space relationship to improve model classification ability. Finally

test this method by classified identified for audio events for family activities. Selecting Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Challenge Task 5 as a dataset for classifier construction and testing

with a final average F1 score of 92.3%

which is superior to other mainstream methods.