{'fraction_non_alphanumeric': 0.015444015444015444, 'fraction_numerical': 0.0, 'mean_word_length': 5.64957264957265, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis and Modeling of the Music Influence and Evolution

Based on big data analysis, we study the comprehensive indicators that measure the influence and revolution of music products, try to provide relative music standards for social to analyze its popular trend. In this paper, we introduce the Decision Tree, Lasso regression model, construct music innovation indicators based on Dijkstra shortest path algorithms, and describe improved models for quantifying music tendency, exploring relevance of genre indicators to brand influence. Also, to target the strategic research of cultural music products and measure the profitability of cultural companies in the field of big data, this paper commits to contribute theoretical support for related companies and departments.