High-dimensional two-sample mean testing is challenging when the dimension exceeds the sample size. The random projection method proposed by Lopes et al. (2011) addresses this difficulty by mapping the data to a lower dimension space where Hotelling's $T^2$ statistic can be applied, while retaining useful covariance information and gaining power when the variables exhibit non-negligible covariance structure. However, single projection tests may be sensitive to the realized projection matrix, whereas existing multiple projection procedures often rely on resampling or simulation for calibration, with limited theoretical understanding. Moreover, projection-based Hotelling tests may be less sensitive to sparse mean differences. To address these limitations, we propose a Cauchy-combined random projection test (CRPT), which applies Hotelling's $T^2$ test after multiple independent random projections and combines the projected $p$-values through the Cauchy transformation. The proposed method retains the ability of random projection methods to incorporate covariance information while reducing reliance on any single projection. Under the Gaussian assumption, we establish the null tail behavior of the proposed statistic and further investigate its asymptotic power under suitable alternatives. To improve sensitivity to sparse alternatives, we further develop a power-enhanced version of CRPT. Simulation studies and real data analysis are conducted to examine the performance and practical applicability of the proposed procedures.

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