Language evolution is cognitively motivated by the reduction of communicative effort. Current research exploring this reported tendency has been constrained by the heavy reliance on manually annotated resources (e.g., dependency parsing) as well as a narrow focus (e.g., syntax as the single metric). To transcend these limitations, we propose two measures: Attention-based Structural Distance (ASD) and Semantic Space Distance (SSD). ASD is a parser-free measure of syntactic locality derived from the attention mechanism of pretrained large language models (LLM), while SSD is a measure of lexical distances that quantify the degree of separation between different parts of speech in the word vector space. Based on multiple diachronic and multilingual corpora, our experiments show a significant decrease of ASD while an increase of SSD, which implies a language developmental trend towards structural compactness and semantic divergence. Our research pioneers a novel lens grounded in LLM for studying language evolution, which has two major contributions. Linguistically, our study corroborates the hypothesized law of human language evolution by demonstrating that its development optimizes syntactic locality as well as functional semantic discriminability. Cognitively, our study shows that human and LLMs share common characteristics in language processing, lending support to the potential of employing LLMs in the study of human cognition.