An object recognition method, wherein the method comprises: obtaining address event representation, AER data of a to-be-recognized object, wherein the AER data comprises a plurality of AER events of the to-be-recognized object, and each AER event comprises a timestamp and address information that are used for generating the AER event; extracting a plurality of feature maps of the AER data, wherein each feature map comprises partial spatial information and partial temporal information of the to-be-recognized object, and the partial spatial information and the partial temporal information are obtained based on the timestamp and the address information of each AER event; and recognizing the to-be-recognized object based on the plurality of feature maps of the AER data, wherein the extracting a plurality of feature maps of the AER data comprises: processing address information of the plurality of AER events by using a plurality of filters, to obtain a plurality of first feature maps; and decaying feature values in the plurality of first feature maps based on timestamps of the plurality of AER events, to obtain the plurality of feature maps of the AER data, characterized in that the recognizing the to-be-recognized object based on the plurality of feature maps of the AER data comprises: encoding the plurality of feature maps of the AER data to obtain a plurality of spike trains, wherein each spike train comprises a plurality of spikes, each spike carries partial temporal information and partial spatial information of the to-be-recognized object, and a plurality of spikes belonging to a same spike train are obtained based on feature values at same locations in feature maps corresponding to different filters that are in a same specified direction; and processing the plurality of spike trains by using a spiking neural network, to recognize the to-be-recognized object. obtaining address event representation, AER data of a to-be-recognized object, wherein the AER data comprises a plurality of AER events of the to-be-recognized object, and each AER event comprises a timestamp and address information that are used for generating the AER event; extracting a plurality of feature maps of the AER data, wherein each feature map comprises partial spatial information and partial temporal information of the to-be-recognized object, and the partial spatial information and the partial temporal information are obtained based on the timestamp and the address information of each AER event; and recognizing the to-be-recognized object based on the plurality of feature maps of the AER data, wherein the extracting a plurality of feature maps of the AER data comprises: processing address information of the plurality of AER events by using a plurality of filters, to obtain a plurality of first feature maps; and decaying feature values in the plurality of first feature maps based on timestamps of the plurality of AER events, to obtain the plurality of feature maps of the AER data, characterized in that the recognizing the to-be-recognized object based on the plurality of feature maps of the AER data comprises: encoding the plurality of feature maps of the AER data to obtain a plurality of spike trains, wherein each spike train comprises a plurality of spikes, each spike carries partial temporal information and partial spatial information of the to-be-recognized object, and a plurality of spikes belonging to a same spike train are obtained based on feature values at same locations in feature maps corresponding to different filters that are in a same specified direction; and processing the plurality of spike trains by using a spiking neural network, to recognize the to-be-recognized object. processing address information of the plurality of AER events by using a plurality of filters, to obtain a plurality of first feature maps; and decaying feature values in the plurality of first feature maps based on timestamps of the plurality of AER events, to obtain the plurality of feature maps of the AER data, characterized in that the recognizing the to-be-recognized object based on the plurality of feature maps of the AER data comprises: encoding the plurality of feature maps of the AER data to obtain a plurality of spike trains, wherein each spike train comprises a plurality of spikes, each spike carries partial temporal information and partial spatial information of the to-be-recognized object, and a plurality of spikes belonging to a same spike train are obtained based on feature values at same locations in feature maps corresponding to different filters that are in a same specified direction; and processing the plurality of spike trains by using a spiking neural network, to recognize the to-be-recognized object. encoding the plurality of feature maps of the AER data to obtain a plurality of spike trains, wherein each spike train comprises a plurality of spikes, each spike carries partial temporal information and partial spatial information of the to-be-recognized object, and a plurality of spikes belonging to a same spike train are obtained based on feature values at same locations in feature maps corresponding to different filters that are in a same specified direction; and processing the plurality of spike trains by using a spiking neural network, to recognize the to-be-recognized object.