Summary: Distributed quantum computation has been considered an alternative and beneficial application in the noisy intermediate-scale quantum (NISQ) era, which needs fewer qubits and quantum gates. In this paper, we consider the Bernstein-Vazirani (BV) problem that needs to identify the hidden string \(s\in\{0, 1\}^n\) of Boolean function \(f_s(x) = \langle s \cdot x \rangle = \left(\sum_{i = 0}^{n - 1}s_i \cdot x_i\right) \mod 2 : \{0, 1\}^n\to\{0, 1\}\). In the first instance, we subtly generate \(t\) subfunctions \(f_{S_{n_j}}(m_j) = \langle S_{n_j} \cdot m_j \rangle: \{0, 1\}^{n_j}\to\{0, 1\}\) according to \(f_s(x)\), where \(S_{n_j}\in\{0, 1\}^{n_j}\), \(\sum_{j = 0}^{t - 1} n_j = n\), and \(j\in\{0, 1, \dots, t - 1\}\). After that, we propose a distributed Bernstein-Vazirani algorithm (DBVA) with \(2 \leq t \leq n\) computing nodes, which will exactly acquire \(s = S_{n_0} S_{n_1} \cdots S_{n_{t - 1}}\in\{0, 1\}^n\). To be more specific, (1) the query complexity of DBVA is \(\mathcal{O}(1)\) rather than \(\mathcal{O}(n)\); (2) the circuit depth of DBVA is \(2^{\max(n_0, n_1, \dots, n_{t - 1})} + 3\) less than the circuit depth of the BV algorithm \(2^n + 3\); (3) we request neither auxiliary qubits nor further classical queries; (4) we explicate how DBVA decomposes a 6-qubit BV algorithm into two 3-qubit or three 2-qubit BV algorithms on MindQuantum (a quantum software), which validates the correctness and practicable of DBVA. Eventually, by simulating the algorithms running in the depolarized channel, it further illustrates that distributed quantum algorithms have the superiority of resisting noise.