Chinese internet companies, such as Tencent, are bringing a new energy to the global market. As one of the largest internet companies in China, Tencent's stock price fluctuations hold significant importance for investors and the market. Accurate forecasting of stock prices is of great importance to make high-yield decisions and manage risk. This paper aims to provide investors with effective and scientific references for decision-making by analyzing various forecasting methods. The dataset China-Techgiant-Stock-Data-in-HK-Market-2022 from Kaggle is used in this paper. This study utilizes various machine learning and time series analysis methods, including linear regression, SVM, random forest, LSTM neural network, and ARIMA, to predict stock prices using historical data. The models' accuracy and performance are compared, with traditional machine learning methods (linear regression, SVM, and random forest) contrasted against deep learning and time series analysis methods (LSTM neural network and ARIMA). It turns out that LSTM has the best prediction results. This can better guide investors in their stock decisions.