An electronic apparatus in a wireless communication system, wherein the electronic apparatus comprises processing circuitry (202, 204) configured to: acquire at least one input parameter and at least one output parameter as training data for a beam adjustment model implemented based on a neural network or a classifier; wherein the training data is predetermined and comprises: training paths as an input parameter of the beam adjustment model, comprising at least different initial positions and movement directions of the user equipment; and beam information as an output parameter of the beam adjustment model, which enables the beam adjustment time interval meeting the performance requirement of the user equipment to be maximized; and train the beam adjustment model for adaptive beamforming with the training data, so that the beam adjustment model outputs, for any movement path of a user equipment, beam information comprising information which enables a beam adjustment time interval meeting a performance requirement of the user equipment, comprising one or more of a signal to interference plus noise ratio, a throughput and an outage probability, to be maximized; wherein the processing circuitry is further configured to generate the training data by: generating training paths comprising different initial positions and movement directions of the user equipment within a coverage area of a serving cell of the user equipment as an input parameter of the beam adjustment model; building a beam database in a codebook-based beamforming manner or an iteration-based non-codebook beamforming manner; searching, for the input parameter, the beam database for the beam information which enables the beam adjustment time interval meeting the performance requirement of the user equipment to be optimized as an output parameter of the beam adjustment model; and generating the training data based on the input parameter and the output parameter. acquire at least one input parameter and at least one output parameter as training data for a beam adjustment model implemented based on a neural network or a classifier; wherein the training data is predetermined and comprises: training paths as an input parameter of the beam adjustment model, comprising at least different initial positions and movement directions of the user equipment; and beam information as an output parameter of the beam adjustment model, which enables the beam adjustment time interval meeting the performance requirement of the user equipment to be maximized; and train the beam adjustment model for adaptive beamforming with the training data, so that the beam adjustment model outputs, for any movement path of a user equipment, beam information comprising information which enables a beam adjustment time interval meeting a performance requirement of the user equipment, comprising one or more of a signal to interference plus noise ratio, a throughput and an outage probability, to be maximized; wherein the processing circuitry is further configured to generate the training data by: generating training paths comprising different initial positions and movement directions of the user equipment within a coverage area of a serving cell of the user equipment as an input parameter of the beam adjustment model; building a beam database in a codebook-based beamforming manner or an iteration-based non-codebook beamforming manner; searching, for the input parameter, the beam database for the beam information which enables the beam adjustment time interval meeting the performance requirement of the user equipment to be optimized as an output parameter of the beam adjustment model; and generating the training data based on the input parameter and the output parameter. generating training paths comprising different initial positions and movement directions of the user equipment within a coverage area of a serving cell of the user equipment as an input parameter of the beam adjustment model; building a beam database in a codebook-based beamforming manner or an iteration-based non-codebook beamforming manner; searching, for the input parameter, the beam database for the beam information which enables the beam adjustment time interval meeting the performance requirement of the user equipment to be optimized as an output parameter of the beam adjustment model; and generating the training data based on the input parameter and the output parameter.