  The recursive intra-frame block partitioning decision process, a crucial
component of the next-generation video coding standards, exerts significant
influence over the encoding time. In this paper, we propose an encoder-decoder
neural network (NN) to accelerate this process. Specifically, a CNN is utilized
to compress the pixel data of the largest coding unit (LCU) into a fixed-length
vector. Subsequently, a Transformer decoder is employed to transcribe the
fixed-length vector into a variable-length vector, which represents the block
partitioning outcomes of the encoding LCU. The vector transcription process
adheres to the constraints imposed by the block partitioning algorithm. By
fully parallelizing the NN prediction in the intra-mode decision, substantial
time savings can be attained during the decision phase. The experimental
results obtained from high-definition (HD) sequences coding demonstrate that
this framework achieves a remarkable 87.84\% reduction in encoding time, with a
relatively small loss (8.09\%) of coding performance compared to AVS3 HPM4.0.
