A quality of experience, QoE, assessment method, wherein the method is applied to a network device, and the method comprises: obtaining m data streams, wherein the m data streams are data streams transmitted by the network device, and m is a natural number greater than 1; screening the m data streams, and determining that p data streams that are in the m data streams and that meet a first preset condition are data streams corresponding to a first type, wherein p is a natural number greater than 0; performing feature extraction on the data streams corresponding to the first type, to obtain known feature vectors corresponding to the first type; training, by using the known feature vectors corresponding to the first type, an untrained first QoE assessment model, to obtain a trained first QoE assessment model; and performing, by using the trained first QoE assessment model, QoE assessment on n data streams corresponding to the first type, to obtain a first QoE assessment result, wherein the n data streams are data streams after the m data streams transmitted by the network device, and n is a natural number greater than 0; wherein after the screening the m data streams, the method further comprises: determining that q data streams that are in the m data streams and that meet a second preset condition are data streams corresponding to a second type, wherein all data streams in the q data streams are different from data streams in the p data streams, q is a natural number greater than 0, and p + q ≤ m; performing feature extraction on the data streams corresponding to the second type, to obtain known feature vectors corresponding to the second type; training, by using the known feature vectors corresponding to the second type, an untrained second QoE assessment model, to obtain a trained second QoE assessment model; and performing, by using the trained second QoE assessment model, QoE assessment on w data streams corresponding to the second type, to obtain a second QoE assessment result, wherein the w data streams are data streams after the m data streams transmitted by the network device, and w is a natural number greater than 0. obtaining m data streams, wherein the m data streams are data streams transmitted by the network device, and m is a natural number greater than 1; screening the m data streams, and determining that p data streams that are in the m data streams and that meet a first preset condition are data streams corresponding to a first type, wherein p is a natural number greater than 0; performing feature extraction on the data streams corresponding to the first type, to obtain known feature vectors corresponding to the first type; training, by using the known feature vectors corresponding to the first type, an untrained first QoE assessment model, to obtain a trained first QoE assessment model; and performing, by using the trained first QoE assessment model, QoE assessment on n data streams corresponding to the first type, to obtain a first QoE assessment result, wherein the n data streams are data streams after the m data streams transmitted by the network device, and n is a natural number greater than 0; wherein after the screening the m data streams, the method further comprises: determining that q data streams that are in the m data streams and that meet a second preset condition are data streams corresponding to a second type, wherein all data streams in the q data streams are different from data streams in the p data streams, q is a natural number greater than 0, and p + q ≤ m; performing feature extraction on the data streams corresponding to the second type, to obtain known feature vectors corresponding to the second type; training, by using the known feature vectors corresponding to the second type, an untrained second QoE assessment model, to obtain a trained second QoE assessment model; and performing, by using the trained second QoE assessment model, QoE assessment on w data streams corresponding to the second type, to obtain a second QoE assessment result, wherein the w data streams are data streams after the m data streams transmitted by the network device, and w is a natural number greater than 0. determining that q data streams that are in the m data streams and that meet a second preset condition are data streams corresponding to a second type, wherein all data streams in the q data streams are different from data streams in the p data streams, q is a natural number greater than 0, and p + q ≤ m; performing feature extraction on the data streams corresponding to the second type, to obtain known feature vectors corresponding to the second type; training, by using the known feature vectors corresponding to the second type, an untrained second QoE assessment model, to obtain a trained second QoE assessment model; and performing, by using the trained second QoE assessment model, QoE assessment on w data streams corresponding to the second type, to obtain a second QoE assessment result, wherein the w data streams are data streams after the m data streams transmitted by the network device, and w is a natural number greater than 0.