The field of tensor decomposition and efficient algorithms is rapidly advancing, with a focus on developing innovative methods for tensor rotation, decomposition, and feature learning. Recent research has led to the development of algorithms that achieve in-place tensor rotation with O(1) auxiliary space and linear time complexity, as well as provably efficient methods for tensor ring decomposition. Additionally, new approaches to genetic algorithms and feature learning models have been proposed, offering improved performance and efficiency. Notable papers in this area include An O(1) Space Algorithm for N-Dimensional Tensor Rotation, A Provably Efficient Method for Tensor Ring Decomposition and Its Applications, and Tensor Network Based Feature Learning Model. These contributions are advancing the algorithmic foundations of tensor decomposition and opening up new opportunities for scalable tensor network computation.