The invention discloses a game level generation recommendation method,
system, device and medium, and the method specifically comprises the steps: carrying out the combined modeling of multi-
modal data through a multi-
task learning network, and generating a dynamic user portrait vector, the main task of the multi-
task learning network is used for predicting the
skill level of a player, and the auxiliary task of the multi-
task learning network is used for predicting the
skill level of the player; auxiliary tasks of the multi-task
learning network are used for classifying interest labels and analyzing emotional tendencies; inputting the dynamic user portrait vector into a dual-channel
generative adversarial network, generating a candidate
level set, and screening initial recommended levels through a playability
evaluation algorithm; and calculating the matching degree between the skill
score of the user and the level challenge degree in real time according to a difficulty-skill
dynamic balance model, and dynamically adjusting the level parameters of the initial recommendation level through the matching degree to obtain a first recommendation level. According to the method, key problems in traditional game
level design and recommendation are effectively solved, and efficient, personalized and real-time game level generation and recommendation with optimized user experience are realized.